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<title>Trust after Tragedy: How Traumatic Events Impact Trust in Government(s)</title>

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) 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<h1 class="title toc-ignore">Trust after Tragedy: How Traumatic Events
Impact Trust in Government(s)</h1>
<h4 class="author">Wayde Z.C. Marsh</h4>
<h4 class="date">07/28/25</h4>

</div>

<div id="TOC">
<ul>
<li><a href="#setting-up-the-data" id="toc-setting-up-the-data">Setting
up the Data</a>
<ul>
<li><a href="#loading-packages-and-data" id="toc-loading-packages-and-data">Loading packages and data</a></li>
</ul></li>
<li><a href="#load-data" id="toc-load-data">Load Data</a></li>
<li><a href="#factor-analysis" id="toc-factor-analysis">Factor
Analysis</a></li>
<li><a href="#descriptives" id="toc-descriptives">Descriptives</a>
<ul>
<li><a href="#figures" id="toc-figures">Figures</a></li>
<li><a href="#tests" id="toc-tests">Tests</a>
<ul>
<li><a href="#regression" id="toc-regression">Regression</a></li>
<li><a href="#sem" id="toc-sem">SEM</a></li>
</ul></li>
</ul></li>
<li><a href="#analysis" id="toc-analysis">Analysis</a>
<ul>
<li><a href="#sem-approach" id="toc-sem-approach">SEM Approach</a></li>
<li><a href="#modeling-voting" id="toc-modeling-voting">Modeling
Voting</a>
<ul>
<li><a href="#path-model-approach" id="toc-path-model-approach">Path
Model Approach</a></li>
</ul></li>
<li><a href="#temporal-proximity" id="toc-temporal-proximity">Temporal
Proximity</a>
<ul>
<li><a href="#regression-approach-ols-and-logit-glm" id="toc-regression-approach-ols-and-logit-glm">Regression Approach (OLS
and Logit GLM)</a></li>
</ul></li>
</ul></li>
</ul>
</div>

<div id="setting-up-the-data" class="section level1 tabset">
<h1 class="tabset">Setting up the Data</h1>
<div id="loading-packages-and-data" class="section level2">
<h2>Loading packages and data</h2>
<pre class="r"><code>##### Packages #####

library(descr)
library(ggplot2)
library(reshape2)
library(psych)
library(GPArotation)
library(dplyr)
library(foreign)
library(ggpubr)
library(ggeffects)
library(data.table)
library(haven)
library(lavaan)
library(tidyverse)
library(modelsummary)
library(fixest)
library(tidySEM)

setwd(&#39;~/Dropbox/Wayde/Dissertation/Trust&#39;)
knitr::opts_knit$set(root.dir = &#39;~/Dropbox/Wayde/Dissertation/Trust&#39;)</code></pre>
</div>
</div>
<div id="load-data" class="section level1">
<h1>Load Data</h1>
<pre class="r"><code>load(&#39;study1.Rda&#39;) # study I
load(&#39;study2.Rda&#39;) # study II
load(&#39;study3.Rda&#39;) # study III
load(&#39;study4_corrected.Rda&#39;) # study IV
load(&#39;study5.Rda&#39;) # study V

dat2$study &lt;- rep(&#39;Two&#39;, nrow(dat2))
dat3$study &lt;- rep(&#39;Three&#39;, nrow(dat3))
dat4$study &lt;- rep(&#39;Four&#39;, nrow(dat4))
dat5$study &lt;- rep(&#39;Five&#39;, nrow(dat5))


datp &lt;- do.call(&#39;rbind&#39;, list(dat2, dat3, dat4, dat5))</code></pre>
</div>
<div id="factor-analysis" class="section level1">
<h1>Factor Analysis</h1>
<pre class="r"><code>fa_vars &lt;- c(&#39;ptsi_1&#39;, &#39;ptsi_2&#39;, &#39;ptsi_3&#39;, &#39;ptsi_4&#39;, &#39;ptsi_5&#39;,
             &#39;ptgi_1&#39;, &#39;ptgi_2&#39;, &#39;ptgi_3&#39;, &#39;ptgi_4&#39;, &#39;ptgi_5&#39;)

fa1 &lt;- dat1[fa_vars]
fa2 &lt;- dat2[fa_vars]
fa3 &lt;- dat3[fa_vars]
fa4 &lt;- dat4[fa_vars]
fa5 &lt;- dat5[fa_vars]

eigen(cor(na.omit(fa1))) # two eigen values &gt; 2 for all confirming 2 factors</code></pre>
<pre><code>## eigen() decomposition
## $values
##  [1] 4.8926972 2.3560567 0.5187440 0.4313146 0.3805756 0.3654700 0.3252488
##  [8] 0.2861781 0.2491343 0.1945807
## 
## $vectors
##             [,1]       [,2]        [,3]        [,4]        [,5]        [,6]
##  [1,] -0.3201020 -0.3456731 -0.33653193 -0.06724578 -0.25094895 -0.14099313
##  [2,] -0.3151732 -0.2998199 -0.05136175  0.19733489  0.80917960  0.02686174
##  [3,] -0.3268970 -0.2915107  0.23060942  0.17831602  0.06631905 -0.26599525
##  [4,] -0.3192697 -0.3435733 -0.33270961 -0.06347543 -0.37840277 -0.19723952
##  [5,] -0.3005118 -0.2791983  0.64759809 -0.33559090 -0.17785589  0.48906182
##  [6,] -0.2902671  0.3582074  0.38921680  0.14563776 -0.04551240 -0.47983784
##  [7,] -0.3186665  0.3526329 -0.11770593  0.21444475 -0.04328693  0.20646800
##  [8,] -0.2864139  0.3283009 -0.20493379 -0.80549380  0.26862934 -0.10802288
##  [9,] -0.3325428  0.3059018  0.12242137  0.15568316 -0.15193912 -0.15856916
## [10,] -0.3473866  0.2358504 -0.28021295  0.26045259 -0.06197620  0.56645786
##              [,7]        [,8]          [,9]       [,10]
##  [1,]  0.29377112 -0.05081302 -0.1569603800  0.68109811
##  [2,]  0.29675207  0.10172003 -0.0031793068 -0.12362333
##  [3,] -0.78774481 -0.10082746  0.0001292895  0.13161605
##  [4,]  0.07079308 -0.01862919  0.1026424702 -0.68360307
##  [5,]  0.15332177  0.01235559 -0.0607808682 -0.03959440
##  [6,]  0.35206708 -0.46701352  0.2008585692 -0.00443526
##  [7,] -0.06240792 -0.15496402 -0.7875302568 -0.14712306
##  [8,] -0.18041976 -0.02319991  0.0472023701  0.01480585
##  [9,]  0.03838198  0.83484037  0.0854828141  0.05144439
## [10,] -0.12126496 -0.19181511  0.5394813049  0.10044094</code></pre>
<pre class="r"><code>eigen(cor(na.omit(fa2)))</code></pre>
<pre><code>## eigen() decomposition
## $values
##  [1] 4.4143207 2.5394117 0.5581223 0.4674742 0.4339335 0.3815066 0.3398545
##  [8] 0.3234624 0.2878409 0.2540733
## 
## $vectors
##             [,1]       [,2]        [,3]        [,4]       [,5]        [,6]
##  [1,] -0.2943857  0.3124619 -0.56442460  0.08557159 -0.5315925 -0.05972320
##  [2,] -0.3233436  0.3189290  0.00763509  0.27685444  0.3339022 -0.05582361
##  [3,] -0.3366452  0.2946678  0.16246765  0.23653773  0.3845729 -0.15546652
##  [4,] -0.3139567  0.3584575 -0.08840250  0.04435814 -0.0870633  0.07721850
##  [5,] -0.2834056  0.3052439  0.58728257 -0.60830412 -0.2492671  0.09298598
##  [6,] -0.2941408 -0.3277294  0.40218739  0.52889660 -0.3955222 -0.01642154
##  [7,] -0.3133238 -0.3656046  0.09220557  0.13574648 -0.1103438  0.32749061
##  [8,] -0.3023274 -0.3391587 -0.17043711 -0.32332363 -0.1328707 -0.63201743
##  [9,] -0.3414782 -0.2834159 -0.03881493 -0.12838732  0.4017859 -0.25486979
## [10,] -0.3519498 -0.2360735 -0.31850125 -0.26248292  0.2015150  0.61855762
##               [,7]        [,8]        [,9]       [,10]
##  [1,] -0.366013916 -0.03363855  0.17100813  0.19826244
##  [2,]  0.167600610 -0.74952709 -0.09382116  0.06112550
##  [3,]  0.008701187  0.47240868  0.56746833 -0.01276523
##  [4,]  0.377774900  0.40655474 -0.63412316 -0.19874917
##  [5,] -0.140104788 -0.11723305  0.05443558  0.06675364
##  [6,] -0.192866718 -0.06038659 -0.03231483 -0.40742181
##  [7,]  0.326352507  0.08497409  0.02576944  0.71233144
##  [8,]  0.436011796 -0.08121070  0.15479232 -0.14906311
##  [9,] -0.584308301  0.11289138 -0.41381393  0.18244196
## [10,] -0.017288881 -0.06834113  0.19480028 -0.42886123</code></pre>
<pre class="r"><code>eigen(cor(na.omit(fa3)))</code></pre>
<pre><code>## eigen() decomposition
## $values
##  [1] 4.1427579 2.6245693 0.5954843 0.4744134 0.4429187 0.3936260 0.3823017
##  [8] 0.3351468 0.3183654 0.2904164
## 
## $vectors
##             [,1]       [,2]        [,3]        [,4]        [,5]        [,6]
##  [1,] -0.3136549  0.2823554 -0.61867246 -0.11253372  0.27310917 -0.34679047
##  [2,] -0.3338815  0.3033224  0.04056615 -0.07688806  0.03804408  0.55562642
##  [3,] -0.3455883  0.2857602  0.23320290  0.03016005  0.15669144  0.40217034
##  [4,] -0.3203677  0.3550201 -0.18770991 -0.03689270 -0.04181702 -0.15769784
##  [5,] -0.3088630  0.2765928  0.62451967 -0.03242547 -0.27957710 -0.54491090
##  [6,] -0.2656426 -0.3796073  0.21086192 -0.42771604  0.55085174 -0.05314994
##  [7,] -0.2884307 -0.3719475  0.08462604  0.29124778  0.34852372 -0.19457971
##  [8,] -0.2796801 -0.3460235 -0.18809106 -0.60071155 -0.54948383  0.04718104
##  [9,] -0.3372852 -0.2958199  0.04824776  0.22155301 -0.15758892  0.20908723
## [10,] -0.3562865 -0.2331973 -0.21592807  0.54808384 -0.26043547 -0.02912845
##              [,7]        [,8]        [,9]       [,10]
##  [1,] -0.06604066 -0.15905439  0.05576355  0.44593167
##  [2,]  0.47874501 -0.48323931  0.08573870 -0.09340818
##  [3,] -0.19655269  0.63295388 -0.08940842  0.33176191
##  [4,] -0.21464522  0.20095047  0.06804344 -0.78551153
##  [5,]  0.06485010 -0.17504001 -0.02139753  0.16318756
##  [6,] -0.11651546 -0.14950424 -0.43732739 -0.15647161
##  [7,]  0.40286687  0.23966035  0.55062682 -0.06494048
##  [8,]  0.16342630  0.23492838  0.11805287  0.07628665
##  [9,] -0.66096924 -0.36565070  0.32274991  0.06336956
## [10,]  0.19510180  0.02916144 -0.60324833 -0.01262376</code></pre>
<pre class="r"><code>eigen(cor(na.omit(fa4)))</code></pre>
<pre><code>## eigen() decomposition
## $values
##  [1] 4.1040286 2.7150418 0.5905253 0.4882206 0.4537739 0.4165985 0.3467126
##  [8] 0.3349175 0.2883350 0.2618460
## 
## $vectors
##             [,1]       [,2]        [,3]        [,4]         [,5]         [,6]
##  [1,] -0.3118265  0.3050928  0.34559608 -0.53317769  0.128130177 -0.332371164
##  [2,] -0.3470820  0.2830417  0.03274351  0.35435939  0.100724495 -0.006254656
##  [3,] -0.3519054  0.2637984 -0.06097150  0.35074345  0.343544144  0.151355906
##  [4,] -0.3261799  0.3613197  0.10630284 -0.11180512  0.090513832  0.020866074
##  [5,] -0.2990034  0.2758340 -0.63489130 -0.15997472 -0.564285913  0.201757937
##  [6,] -0.2564546 -0.3584158 -0.52453208 -0.19878205  0.549272196 -0.250823562
##  [7,] -0.2894356 -0.3857141 -0.09425081 -0.05369591 -0.230189096 -0.369231144
##  [8,] -0.2870796 -0.3387775  0.22460666 -0.40817712  0.056908525  0.726939043
##  [9,] -0.3159151 -0.3260190  0.09762182  0.45233004  0.007447094  0.149588197
## [10,] -0.3618087 -0.2286570  0.34262391  0.12421409 -0.413306550 -0.274552221
##               [,7]        [,8]         [,9]       [,10]
##  [1,] -0.155389461  0.37542832  0.012669915  0.33738217
##  [2,] -0.567279973 -0.53085253  0.071637502  0.23205889
##  [3,]  0.641020475  0.07825966 -0.198475751  0.28719126
##  [4,]  0.028642824 -0.06247387 -0.145843146 -0.83942380
##  [5,] -0.021991757  0.16381660  0.107628734  0.08834814
##  [6,] -0.014819738 -0.06198848  0.336514438 -0.09593944
##  [7,]  0.005713692 -0.18090568 -0.728908006  0.04795957
##  [8,] -0.002573987 -0.20995615 -0.033633838  0.09412758
##  [9,] -0.341685379  0.65257966  0.001264871 -0.12138370
## [10,]  0.353310705 -0.18405023  0.526097963 -0.04803041</code></pre>
<pre class="r"><code>eigen(cor(na.omit(fa5)))</code></pre>
<pre><code>## eigen() decomposition
## $values
##  [1] 4.0861731 2.5512757 0.6078930 0.5266476 0.4969015 0.4305150 0.3997319
##  [8] 0.3236108 0.3091045 0.2681470
## 
## $vectors
##             [,1]       [,2]        [,3]        [,4]        [,5]        [,6]
##  [1,] -0.2956097  0.3200903  0.44252079  0.06049332 -0.61991863  0.22259226
##  [2,] -0.3412977  0.3065650 -0.02538820  0.08641849  0.29924121 -0.27282434
##  [3,] -0.3385223  0.2818746 -0.11272522  0.34377810  0.50199720  0.15304031
##  [4,] -0.3319428  0.3590531  0.17745727  0.03102383 -0.05086467  0.03853952
##  [5,] -0.2978955  0.2621433 -0.66246618 -0.47949520 -0.24175537 -0.16636345
##  [6,] -0.2694619 -0.3392329 -0.40043806  0.60787959 -0.37777867  0.07504342
##  [7,] -0.2944520 -0.3715564  0.09581689  0.15469985 -0.03274905 -0.55136758
##  [8,] -0.3038154 -0.3335098  0.06858659 -0.44538807 -0.03456519  0.03118586
##  [9,] -0.3168287 -0.3192606 -0.07100289 -0.14883151  0.21293423  0.68720433
## [10,] -0.3613998 -0.2453149  0.37575822 -0.16074800  0.15098534 -0.20143502
##              [,7]        [,8]         [,9]        [,10]
##  [1,]  0.08000170 -0.01690406 -0.003485009  0.412466315
##  [2,] -0.25689666 -0.71682700 -0.124800031  0.148427738
##  [3,] -0.03466568  0.53004321  0.144718721  0.310717582
##  [4,] -0.07084631  0.17891386 -0.252167109 -0.789779209
##  [5,]  0.26032772  0.07905591  0.107998993  0.044220933
##  [6,] -0.20211637 -0.11684500  0.231450691 -0.161312302
##  [7,]  0.31626195  0.20046052 -0.525847083  0.142587139
##  [8,] -0.72636175  0.21509262 -0.067478286  0.113163385
##  [9,]  0.33481271 -0.24652060 -0.282746914 -0.009563205
## [10,]  0.27025694 -0.07611235  0.688210398 -0.162291347</code></pre>
<pre class="r"><code>## Parallel Analysis

sem.parallel&lt;-function(x, R=1000, percent=.95){
  N&lt;-nrow(x)
  P&lt;-ncol(x)
  
  # original eigenvalues
  orig.eigen&lt;-eigen(cor(na.omit(x)))$values
  
  pa.eigen&lt;-NULL
  
  for (i in 1:R){
    temp&lt;-array(rnorm(N*P), c(N,P))
    temp.eigen&lt;-eigen(cor(temp))
    pa.eigen&lt;-rbind(pa.eigen, temp.eigen$values)
  }
  
  m.eigen&lt;-apply(pa.eigen, 2, mean)
  p.eigen&lt;-apply(pa.eigen, 2, quantile, prob=percent)
  ## plot the eigen values
  plot(orig.eigen, type=&#39;b&#39;, xlab=&#39;Number of factors&#39;, ylab=&#39;Eigenvalues&#39;)
  lines(m.eigen, lty=3)
  lines(p.eigen, lty=2)
  abline(h = 1, col = &#39;red&#39;)
  legend(&#39;topright&#39;, c(&#39;Data&#39;, &#39;Average&#39;, paste(percent*100, &#39;%&#39;, sep=&#39;&#39;)), lty=c(1, 3, 2))
  invisible(list(eigen=orig.eigen, pa.eigen=pa.eigen))
}

set.seed(52617)

sem.parallel(fa1) # Parallel Analysis confirms 2 factors for all dfs</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>sem.parallel(fa2)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>sem.parallel(fa3)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>sem.parallel(fa4)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>sem.parallel(fa5)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code># EFA

### EFA ####
fit1 &lt;- fa(fa1, nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit2 &lt;- fa(fa2, nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit3 &lt;- fa(fa3, nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit4 &lt;- fa(fa4, nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit5 &lt;- fa(fa5, nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)

fit1s &lt;- fa(fa1[, 1:5], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit2s &lt;- fa(fa2[, 1:5], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit3s &lt;- fa(fa3[, 1:5], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit4s &lt;- fa(fa4[, 1:5], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit5s &lt;- fa(fa5[, 1:5], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)

fit1g &lt;- fa(fa1[, 6:10], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit2g &lt;- fa(fa2[, 6:10], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit3g &lt;- fa(fa3[, 6:10], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit4g &lt;- fa(fa4[, 6:10], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)
fit5g &lt;- fa(fa5[, 6:10], nfactors = 2, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, alpha = 0.05)

print(fit1$loadings, cutoff = 0.6)</code></pre>
<pre><code>## 
## Loadings:
##        MR1    MR2   
## ptsi_1  0.878       
## ptsi_2  0.773       
## ptsi_3  0.787       
## ptsi_4  0.871       
## ptsi_5  0.705       
## ptgi_1         0.816
## ptgi_2         0.884
## ptgi_3         0.750
## ptgi_4         0.828
## ptgi_5         0.744
## 
##                  MR1   MR2
## SS loadings    3.276 3.257
## Proportion Var 0.328 0.326
## Cumulative Var 0.328 0.653</code></pre>
<pre class="r"><code>print(fit2$loadings, cutoff = 0.6)</code></pre>
<pre><code>## 
## Loadings:
##        MR1    MR2   
## ptsi_1         0.728
## ptsi_2         0.806
## ptsi_3         0.793
## ptsi_4         0.863
## ptsi_5         0.692
## ptgi_1  0.753       
## ptgi_2  0.874       
## ptgi_3  0.792       
## ptgi_4  0.784       
## ptgi_5  0.731       
## 
##                  MR1   MR2
## SS loadings    3.114 3.067
## Proportion Var 0.311 0.307
## Cumulative Var 0.311 0.618</code></pre>
<pre class="r"><code>print(fit3$loadings, cutoff = 0.6)</code></pre>
<pre><code>## 
## Loadings:
##        MR1    MR2   
## ptsi_1  0.713       
## ptsi_2  0.792       
## ptsi_3  0.789       
## ptsi_4  0.860       
## ptsi_5  0.692       
## ptgi_1         0.774
## ptgi_2         0.811
## ptgi_3         0.737
## ptgi_4         0.772
## ptgi_5         0.705
## 
##                  MR1   MR2
## SS loadings    3.035 2.903
## Proportion Var 0.303 0.290
## Cumulative Var 0.303 0.594</code></pre>
<pre class="r"><code>print(fit4$loadings, cutoff = 0.6)</code></pre>
<pre><code>## 
## Loadings:
##        MR1    MR2   
## ptsi_1  0.748       
## ptsi_2  0.790       
## ptsi_3  0.769       
## ptsi_4  0.894       
## ptsi_5  0.674       
## ptgi_1         0.722
## ptgi_2         0.851
## ptgi_3         0.745
## ptgi_4         0.783
## ptgi_5         0.712
## 
##                  MR1   MR2
## SS loadings    3.085 2.936
## Proportion Var 0.308 0.294
## Cumulative Var 0.308 0.602</code></pre>
<pre class="r"><code>print(fit5$loadings, cutoff = 0.6)</code></pre>
<pre><code>## 
## Loadings:
##        MR1    MR2   
## ptsi_1  0.721       
## ptsi_2  0.799       
## ptsi_3  0.750       
## ptsi_4  0.885       
## ptsi_5  0.635       
## ptgi_1         0.698
## ptgi_2         0.813
## ptgi_3         0.760
## ptgi_4         0.762
## ptgi_5         0.736
## 
##                  MR1   MR2
## SS loadings    2.942 2.860
## Proportion Var 0.294 0.286
## Cumulative Var 0.294 0.580</code></pre>
<pre class="r"><code>psych::alpha(fa1[, 1:5])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa1[, 1:5])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##        0.9       0.9    0.89      0.65 9.3 0.004 0.33 0.28     0.65
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.89   0.9  0.91
## Duhachek  0.89   0.9  0.91
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptsi_1      0.87      0.87    0.84      0.63 6.8   0.0053 0.0014  0.64
## ptsi_2      0.88      0.89    0.86      0.66 7.7   0.0049 0.0058  0.64
## ptsi_3      0.88      0.88    0.86      0.65 7.3   0.0051 0.0067  0.62
## ptsi_4      0.87      0.87    0.84      0.63 6.9   0.0053 0.0013  0.64
## ptsi_5      0.90      0.90    0.87      0.68 8.6   0.0044 0.0033  0.66
## 
##  Item statistics 
##           n raw.r std.r r.cor r.drop mean   sd
## ptsi_1 1268  0.87  0.88  0.85   0.80 0.28 0.32
## ptsi_2 1268  0.83  0.84  0.78   0.74 0.26 0.32
## ptsi_3 1268  0.86  0.85  0.80   0.76 0.41 0.35
## ptsi_4 1268  0.87  0.87  0.85   0.79 0.30 0.33
## ptsi_5 1267  0.81  0.80  0.72   0.69 0.41 0.35
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptsi_1 0.46 0.14 0.13 0.14 0.07 0.05 0.18
## ptsi_2 0.47 0.16 0.11 0.12 0.08 0.05 0.18
## ptsi_3 0.30 0.13 0.13 0.19 0.15 0.10 0.18
## ptsi_4 0.44 0.14 0.13 0.14 0.08 0.07 0.18
## ptsi_5 0.30 0.12 0.14 0.20 0.13 0.11 0.19</code></pre>
<pre class="r"><code>psych::alpha(fa2[, 1:5])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa2[, 1:5])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.88      0.88    0.86       0.6 7.6 0.0058 0.33 0.28     0.59
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.87  0.88  0.89
## Duhachek  0.87  0.88  0.90
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptsi_1      0.87      0.87    0.84      0.63 6.7   0.0068 0.0029  0.63
## ptsi_2      0.85      0.85    0.82      0.59 5.8   0.0076 0.0046  0.59
## ptsi_3      0.85      0.85    0.82      0.59 5.8   0.0077 0.0044  0.59
## ptsi_4      0.84      0.84    0.81      0.58 5.4   0.0080 0.0041  0.57
## ptsi_5      0.88      0.88    0.84      0.64 7.0   0.0064 0.0023  0.66
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptsi_1 765  0.79  0.80  0.72   0.67 0.31 0.32
## ptsi_2 765  0.84  0.85  0.80   0.75 0.29 0.33
## ptsi_3 765  0.85  0.85  0.80   0.75 0.38 0.35
## ptsi_4 765  0.87  0.87  0.83   0.78 0.31 0.34
## ptsi_5 765  0.78  0.78  0.69   0.65 0.37 0.35
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptsi_1 0.39 0.14 0.20 0.14 0.07 0.06 0.23
## ptsi_2 0.46 0.13 0.14 0.13 0.07 0.07 0.23
## ptsi_3 0.34 0.13 0.16 0.16 0.10 0.11 0.23
## ptsi_4 0.43 0.13 0.14 0.12 0.10 0.08 0.23
## ptsi_5 0.35 0.13 0.13 0.18 0.11 0.10 0.23</code></pre>
<pre class="r"><code>psych::alpha(fa3[, 1:5])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa3[, 1:5])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.88      0.88    0.86      0.59 7.3 0.006 0.33 0.28     0.59
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.87  0.88  0.89
## Duhachek  0.87  0.88  0.89
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptsi_1      0.87      0.87    0.83      0.62 6.5   0.0069 0.0012  0.62
## ptsi_2      0.85      0.85    0.82      0.58 5.6   0.0078 0.0062  0.59
## ptsi_3      0.85      0.85    0.81      0.58 5.5   0.0080 0.0056  0.58
## ptsi_4      0.84      0.84    0.80      0.57 5.3   0.0083 0.0047  0.58
## ptsi_5      0.87      0.87    0.84      0.62 6.6   0.0068 0.0021  0.65
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptsi_1 732  0.78  0.79  0.71   0.66 0.31 0.33
## ptsi_2 731  0.83  0.84  0.78   0.74 0.27 0.33
## ptsi_3 732  0.84  0.84  0.79   0.74 0.38 0.35
## ptsi_4 732  0.86  0.86  0.82   0.77 0.31 0.35
## ptsi_5 732  0.78  0.78  0.70   0.65 0.36 0.35
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptsi_1 0.42 0.12 0.14 0.15 0.11 0.06 0.27
## ptsi_2 0.46 0.17 0.12 0.11 0.07 0.07 0.27
## ptsi_3 0.33 0.15 0.12 0.17 0.11 0.11 0.27
## ptsi_4 0.46 0.12 0.12 0.14 0.08 0.09 0.27
## ptsi_5 0.37 0.11 0.16 0.15 0.11 0.10 0.27</code></pre>
<pre class="r"><code>psych::alpha(fa4[, 1:5])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa4[, 1:5])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.88      0.88    0.86       0.6 7.6 0.0058 0.29 0.27     0.59
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.87  0.88  0.89
## Duhachek  0.87  0.88  0.89
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptsi_1      0.86      0.87    0.83      0.62 6.4   0.0070 0.0035  0.62
## ptsi_2      0.85      0.85    0.82      0.59 5.8   0.0076 0.0055  0.58
## ptsi_3      0.86      0.86    0.82      0.60 6.0   0.0075 0.0056  0.59
## ptsi_4      0.84      0.84    0.80      0.57 5.2   0.0083 0.0027  0.56
## ptsi_5      0.88      0.88    0.85      0.64 7.1   0.0064 0.0028  0.66
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptsi_1 752  0.80  0.81  0.74   0.69 0.26 0.31
## ptsi_2 752  0.84  0.84  0.79   0.74 0.26 0.32
## ptsi_3 752  0.83  0.83  0.78   0.73 0.32 0.33
## ptsi_4 752  0.88  0.88  0.85   0.80 0.28 0.33
## ptsi_5 752  0.78  0.77  0.68   0.64 0.34 0.34
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptsi_1 0.50 0.12 0.12 0.14 0.06 0.05 0.25
## ptsi_2 0.49 0.15 0.13 0.10 0.07 0.06 0.25
## ptsi_3 0.40 0.14 0.14 0.14 0.11 0.07 0.25
## ptsi_4 0.48 0.12 0.13 0.12 0.08 0.07 0.25
## ptsi_5 0.39 0.13 0.15 0.16 0.09 0.08 0.25</code></pre>
<pre class="r"><code>psych::alpha(fa5[, 1:5])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa5[, 1:5])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.87      0.87    0.85      0.58 6.8 0.0064 0.28 0.26     0.55
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.86  0.87  0.88
## Duhachek  0.86  0.87  0.88
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptsi_1      0.85      0.86    0.82      0.60 5.9   0.0075 0.0052  0.60
## ptsi_2      0.83      0.83    0.80      0.56 5.0   0.0086 0.0078  0.53
## ptsi_3      0.84      0.84    0.81      0.57 5.4   0.0081 0.0073  0.55
## ptsi_4      0.82      0.82    0.78      0.54 4.6   0.0092 0.0046  0.53
## ptsi_5      0.87      0.87    0.84      0.62 6.5   0.0069 0.0049  0.65
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptsi_1 773  0.78  0.78  0.71   0.65 0.26 0.31
## ptsi_2 773  0.84  0.84  0.80   0.74 0.23 0.30
## ptsi_3 773  0.82  0.82  0.76   0.70 0.31 0.33
## ptsi_4 773  0.87  0.87  0.85   0.79 0.26 0.33
## ptsi_5 773  0.75  0.75  0.65   0.61 0.33 0.33
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptsi_1 0.49 0.12 0.16 0.11 0.07 0.05 0.23
## ptsi_2 0.54 0.14 0.12 0.11 0.05 0.05 0.23
## ptsi_3 0.42 0.16 0.13 0.13 0.08 0.08 0.23
## ptsi_4 0.53 0.12 0.11 0.10 0.09 0.06 0.23
## ptsi_5 0.36 0.16 0.16 0.16 0.09 0.07 0.23</code></pre>
<pre class="r"><code>psych::alpha(fa1[, 6:10])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa1[, 6:10])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##        0.9       0.9    0.89      0.65 9.4 0.0039 0.44 0.29     0.66
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt      0.9   0.9  0.91
## Duhachek   0.9   0.9  0.91
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## ptgi_1      0.89      0.89    0.86      0.66 7.8   0.0048 0.0027  0.66
## ptgi_2      0.87      0.87    0.84      0.63 6.7   0.0054 0.0019  0.61
## ptgi_3      0.89      0.89    0.87      0.68 8.5   0.0044 0.0022  0.69
## ptgi_4      0.87      0.88    0.85      0.64 7.0   0.0052 0.0031  0.62
## ptgi_5      0.88      0.88    0.85      0.66 7.6   0.0049 0.0022  0.66
## 
##  Item statistics 
##           n raw.r std.r r.cor r.drop mean   sd
## ptgi_1 1269  0.83  0.84  0.78   0.74 0.57 0.33
## ptgi_2 1269  0.88  0.88  0.86   0.81 0.39 0.33
## ptgi_3 1269  0.82  0.81  0.74   0.70 0.37 0.35
## ptgi_4 1269  0.87  0.87  0.83   0.79 0.51 0.35
## ptgi_5 1269  0.84  0.84  0.79   0.75 0.35 0.34
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptgi_1 0.14 0.09 0.13 0.23 0.23 0.18 0.18
## ptgi_2 0.30 0.14 0.15 0.21 0.13 0.07 0.18
## ptgi_3 0.38 0.12 0.11 0.16 0.14 0.09 0.18
## ptgi_4 0.21 0.09 0.13 0.21 0.21 0.15 0.18
## ptgi_5 0.35 0.15 0.14 0.16 0.12 0.07 0.18</code></pre>
<pre class="r"><code>psych::alpha(fa2[, 6:10])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa2[, 6:10])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.89      0.89    0.87      0.62 8.3 0.0054 0.51 0.29     0.64
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.88  0.89   0.9
## Duhachek  0.88  0.89   0.9
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## ptgi_1      0.88      0.88    0.85      0.64 7.2   0.0063 0.00038  0.65
## ptgi_2      0.86      0.86    0.82      0.60 6.0   0.0073 0.00241  0.60
## ptgi_3      0.87      0.87    0.84      0.63 6.7   0.0067 0.00349  0.65
## ptgi_4      0.86      0.87    0.83      0.62 6.4   0.0070 0.00375  0.63
## ptgi_5      0.87      0.87    0.84      0.63 6.9   0.0066 0.00178  0.64
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptgi_1 765  0.80  0.81  0.74   0.69 0.60 0.31
## ptgi_2 765  0.87  0.87  0.84   0.79 0.48 0.34
## ptgi_3 765  0.84  0.83  0.77   0.73 0.48 0.37
## ptgi_4 765  0.85  0.85  0.80   0.75 0.58 0.34
## ptgi_5 765  0.83  0.82  0.76   0.72 0.43 0.36
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptgi_1 0.11 0.08 0.15 0.24 0.23 0.19 0.23
## ptgi_2 0.22 0.10 0.17 0.22 0.16 0.13 0.23
## ptgi_3 0.28 0.09 0.10 0.19 0.20 0.15 0.23
## ptgi_4 0.16 0.08 0.11 0.22 0.22 0.21 0.23
## ptgi_5 0.31 0.09 0.13 0.21 0.14 0.13 0.23</code></pre>
<pre class="r"><code>psych::alpha(fa3[, 6:10])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa3[, 6:10])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.87      0.87    0.85      0.58 6.9 0.0064 0.51 0.27     0.58
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.86  0.87  0.88
## Duhachek  0.86  0.87  0.88
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## ptgi_1      0.85      0.85    0.81      0.59 5.7   0.0078 0.00107  0.59
## ptgi_2      0.84      0.84    0.80      0.57 5.2   0.0083 0.00196  0.57
## ptgi_3      0.85      0.85    0.82      0.59 5.8   0.0077 0.00248  0.61
## ptgi_4      0.84      0.84    0.80      0.57 5.2   0.0084 0.00214  0.56
## ptgi_5      0.85      0.85    0.81      0.59 5.7   0.0078 0.00066  0.58
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptgi_1 732  0.80  0.80  0.74   0.68 0.61 0.31
## ptgi_2 732  0.83  0.83  0.78   0.73 0.48 0.32
## ptgi_3 732  0.81  0.80  0.72   0.68 0.48 0.36
## ptgi_4 732  0.83  0.83  0.78   0.73 0.58 0.33
## ptgi_5 732  0.81  0.80  0.74   0.69 0.42 0.34
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptgi_1 0.11 0.08 0.12 0.25 0.24 0.20 0.27
## ptgi_2 0.19 0.10 0.16 0.28 0.17 0.09 0.27
## ptgi_3 0.25 0.09 0.14 0.18 0.18 0.15 0.27
## ptgi_4 0.15 0.08 0.12 0.23 0.23 0.19 0.27
## ptgi_5 0.29 0.10 0.16 0.21 0.12 0.11 0.27</code></pre>
<pre class="r"><code>psych::alpha(fa4[, 6:10])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa4[, 6:10])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.87      0.87    0.85      0.58   7 0.0063 0.52 0.28     0.59
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.86  0.87  0.89
## Duhachek  0.86  0.87  0.89
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## ptgi_1      0.86      0.86    0.82      0.61 6.2   0.0072 0.00065  0.61
## ptgi_2      0.83      0.83    0.79      0.56 5.0   0.0086 0.00282  0.56
## ptgi_3      0.85      0.85    0.82      0.59 5.7   0.0077 0.00460  0.62
## ptgi_4      0.84      0.84    0.81      0.57 5.3   0.0082 0.00399  0.58
## ptgi_5      0.85      0.85    0.81      0.59 5.8   0.0077 0.00146  0.59
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptgi_1 752  0.77  0.78  0.70   0.65 0.59 0.32
## ptgi_2 752  0.85  0.86  0.82   0.76 0.48 0.33
## ptgi_3 752  0.81  0.81  0.74   0.69 0.51 0.36
## ptgi_4 752  0.83  0.83  0.78   0.73 0.60 0.33
## ptgi_5 752  0.81  0.81  0.74   0.69 0.43 0.36
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptgi_1 0.13 0.09 0.13 0.21 0.26 0.18 0.25
## ptgi_2 0.22 0.08 0.18 0.23 0.17 0.12 0.25
## ptgi_3 0.24 0.08 0.14 0.17 0.18 0.19 0.25
## ptgi_4 0.14 0.07 0.12 0.20 0.25 0.21 0.25
## ptgi_5 0.30 0.09 0.15 0.20 0.12 0.14 0.25</code></pre>
<pre class="r"><code>psych::alpha(fa5[, 6:10])</code></pre>
<pre><code>## 
## Reliability analysis   
## Call: psych::alpha(x = fa5[, 6:10])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.87      0.87    0.85      0.57 6.6 0.0066 0.51 0.28     0.58
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.85  0.87  0.88
## Duhachek  0.85  0.87  0.88
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## ptgi_1      0.85      0.86    0.82      0.60 5.9   0.0075 0.00044  0.60
## ptgi_2      0.83      0.83    0.79      0.55 5.0   0.0086 0.00307  0.57
## ptgi_3      0.84      0.84    0.80      0.57 5.2   0.0083 0.00322  0.58
## ptgi_4      0.84      0.84    0.80      0.56 5.2   0.0084 0.00368  0.58
## ptgi_5      0.84      0.84    0.80      0.56 5.2   0.0084 0.00098  0.58
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## ptgi_1 773  0.75  0.77  0.68   0.63 0.61 0.31
## ptgi_2 773  0.83  0.83  0.78   0.73 0.46 0.32
## ptgi_3 773  0.82  0.81  0.75   0.70 0.48 0.37
## ptgi_4 773  0.82  0.82  0.75   0.70 0.58 0.34
## ptgi_5 773  0.82  0.82  0.76   0.71 0.40 0.36
## 
## Non missing response frequency for each item
##           0  0.2  0.4  0.6  0.8    1 miss
## ptgi_1 0.11 0.06 0.14 0.26 0.23 0.20 0.23
## ptgi_2 0.22 0.09 0.19 0.25 0.15 0.10 0.23
## ptgi_3 0.28 0.07 0.13 0.17 0.18 0.17 0.23
## ptgi_4 0.17 0.05 0.14 0.20 0.24 0.20 0.23
## ptgi_5 0.35 0.09 0.13 0.18 0.13 0.11 0.23</code></pre>
</div>
<div id="descriptives" class="section level1">
<h1>Descriptives</h1>
<pre class="r"><code>datp$tr_type &lt;- ifelse(datp$trauma1_1 == 1, &#39;Sexual Violence&#39;,
                ifelse(datp$trauma1_2 == 1, &#39;Natural Disaster&#39;,
                ifelse(datp$trauma1_3 == 1, &#39;Industrial Disaster&#39;,
                ifelse(datp$trauma1_4 == 1, &#39;School Shooting&#39;,
                ifelse(datp$trauma1_5 == 1, &#39;Church Shooting&#39;,
                ifelse(datp$trauma1_6 == 1, &#39;Other Mass Shooting&#39;,
                ifelse(datp$trauma1_7 == 1, &#39;Car Accident&#39;,
                ifelse(datp$trauma1_8 == 1, &#39;Plane Accident&#39;,
                ifelse(datp$trauma1_9 == 1, &#39;Assault&#39;,
                ifelse(datp$trauma1_10 == 1, &#39;Warfare&#39;,
                ifelse(datp$trauma1_11 == 1, &#39;Other&#39;, &#39;None&#39;)))))))))))

datp$rtr_type &lt;- ifelse(datp$trauma2_1 == 1, &#39;Sexual Violence&#39;,
                 ifelse(datp$trauma2_2 == 1, &#39;Natural Disaster&#39;,
                 ifelse(datp$trauma2_3 == 1, &#39;Industrial Disaster&#39;,
                 ifelse(datp$trauma2_4 == 1, &#39;School Shooting&#39;,
                 ifelse(datp$trauma2_5 == 1, &#39;Church Shooting&#39;,
                 ifelse(datp$trauma2_6 == 1, &#39;Other Mass Shooting&#39;,
                 ifelse(datp$trauma2_7 == 1, &#39;Car Accident&#39;,
                 ifelse(datp$trauma2_8 == 1, &#39;Plane Accident&#39;,
                 ifelse(datp$trauma2_9 == 1, &#39;Assault&#39;,
                 ifelse(datp$trauma2_10 == 1, &#39;Warfare&#39;,
                 ifelse(datp$trauma2_11 == 1, &#39;Other&#39;, &#39;None&#39;)))))))))))

datp &lt;- datp %&gt;%
  mutate(tr_type_simp = recode(tr_type, 
                               &#39;Sexual Violence&#39; = 4,
                               &#39;Natural Disaster&#39; = 1,
                               &#39;Industrial Disaster&#39; = 2,
                               &#39;School Shooting&#39; = 4,
                               &#39;Church Shooting&#39; = 4,
                               &#39;Other Mass Shooting&#39; = 4,
                               &#39;Car Accident&#39; = 3,
                               &#39;Plane Accident&#39; = 3,
                               &#39;Assault&#39; = 4,
                               &#39;Warfare&#39; = 4,
                               &#39;Other&#39; = 2,
                               &#39;None&#39; = 0))

datp &lt;- datp %&gt;%
  mutate(rtr_type_simp = recode(rtr_type, 
                               &#39;Sexual Violence&#39; = 4,
                               &#39;Natural Disaster&#39; = 1,
                               &#39;Industrial Disaster&#39; = 2,
                               &#39;School Shooting&#39; = 4,
                               &#39;Church Shooting&#39; = 4,
                               &#39;Other Mass Shooting&#39; = 4,
                               &#39;Car Accident&#39; = 3,
                               &#39;Plane Accident&#39; = 3,
                               &#39;Assault&#39; = 4,
                               &#39;Warfare&#39; = 4,
                               &#39;Other&#39; = 2,
                               &#39;None&#39; = 0))</code></pre>
<div id="figures" class="section level2">
<h2>Figures</h2>
<pre class="r"><code>plot_type_df &lt;- data.frame(type = c(&#39;aNone&#39;, &#39;bNatural Disaster&#39;, &#39;cIndustrial Disaster&#39;,
                                    &#39;dCar Accident&#39;, &#39;ePlane Accident&#39;, 
                                    &#39;fSchool Shooting&#39;, &#39;gChurch Shooting&#39;,
                                    &#39;hOther Mass Shooting&#39;, &#39;iAssault&#39;, &#39;jWarfare&#39;,
                                    &#39;kSexual Violence&#39;, &#39;lOther&#39;),
                           experienced = c(rep(&#39;Respondent&#39;, 12), 
                                           rep(&#39;Friend/Family Member&#39;, 12)),
                           exposed = c(41.0, 21.4, 1.5, 8.4, 0.2, 0.8, 0.2, 0.4, 1.6, 0.9,
                                    21.7, 2.0, 36.6, 16.6, 1.1, 8.1, 0.2, 0.4, 0.1, 0.5, 2.1,
                                    2.9, 30.2, 1.0))

p_type1 &lt;- ggplot(plot_type_df, aes(x = type, y = exposed, fill = experienced)) +
  theme_bw() +
  geom_bar(stat = &#39;identity&#39;, position = position_dodge()) +
  scale_fill_manual(name = &#39;Who Experienced&#39;,
                    values = c(&#39;grey65&#39;, &#39;gray30&#39;)) +
  scale_x_discrete(name = &#39;Type of Traumatic Event&#39;, 
                   labels = c(&#39;None&#39;, &#39;Natural Disaster&#39;, &#39;Industrial Disaster&#39;,
                                    &#39;Car Accident&#39;, &#39;Plane Accident&#39;, 
                                    &#39;School Shooting&#39;, &#39;Church Shooting&#39;,
                                    &#39;Other Mass Shooting&#39;, &#39;Assault&#39;, &#39;Warfare&#39;,
                                    &#39;Sexual Violence&#39;, &#39;Other&#39;)) +
  ylim(c(0, 50)) +
  ylab(&#39;Percentage of Sample Experienced&#39;) +
  labs(title = &#39;Sexual Violence and Natural Disasters more Common Traumatic Events Experienced&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# ggsave(&#39;p_type1.png&#39;, p_type1, &#39;png&#39;, &#39;~/Dropbox/Wayde/Dissertation/Trust&#39;,
#     width = 190, height = 100, units = &#39;mm&#39;)

plot_type2_df &lt;- data.frame(type = c(&#39;aNone&#39;, &#39;bNatural Disaster&#39;, &#39;cAnthropogenic Disaster&#39;,
                                    &#39;dAnthropogenic Interpersonal Accidental&#39;, 
                                    &#39;eAnthropogenic Interpersonal Intentional&#39;),
                           experienced = c(rep(&#39;Respondent&#39;, 5), 
                                           rep(&#39;Friend/Family Member&#39;, 5)),
                           exposed = c(41.0, 21.4, 3.5, 8.5, 25.5,
                                       36.6, 16.6, 2.1, 8.3, 36.3))

p_type2 &lt;- ggplot(plot_type2_df, aes(x = type, y = exposed, fill = experienced)) +
  theme_bw() +
  geom_bar(stat = &#39;identity&#39;, position = position_dodge()) +
  scale_fill_manual(name = &#39;Who Experienced&#39;,
                    values = c(&#39;grey65&#39;, &#39;gray30&#39;)) +
  scale_x_discrete(name = &#39;\nType of Traumatic Event&#39;, 
                   labels = c(&#39;None&#39;, &#39;Natural Disaster&#39;, &#39;Anthropogenic \nDisaster&#39;,
                                    &#39;Anthropogenic \nInterpersonal \nAccidental&#39;, 
                                    &#39;Anthropogenic \nInterpersonal \nIntentional&#39;)) +
  ylim(c(0, 50)) +
  ylab(&#39;Percentage of Sample Experienced&#39;) +
  labs(title = &#39;Prevalence of Exposure to Traumatic Events, by Type&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# ggsave(&#39;p_type2.png&#39;, p_type2, &#39;png&#39;, &#39;~/Dropbox/Wayde/Dissertation/Trust&#39;,
#     width = 190, height = 100, units = &#39;mm&#39;)

## PTS and PTG by type

df_tr &lt;- datp %&gt;%
  group_by(tr_type_simp) %&gt;%
  summarize(avg = mean(ptsi, na.rm = T))

df_tr2 &lt;- datp %&gt;%
  group_by(tr_type_simp) %&gt;%
  summarize(avg = mean(ptgi, na.rm = T))

df_rtr &lt;- datp %&gt;%
  group_by(rtr_type_simp) %&gt;%
  summarize(avg = mean(ptsi, na.rm = T))

df_rtr2 &lt;- datp %&gt;%
  group_by(rtr_type_simp) %&gt;%
  summarize(avg = mean(ptgi, na.rm = T))

df_tr &lt;- rbind(df_tr, df_tr2)
df_rtr &lt;- rbind(df_rtr, df_rtr2)

df_tr$exp &lt;- rep(&#39;Respondent&#39;, 10)
df_rtr$exp &lt;- rep(&#39;Close Friend/Family Member&#39;, 10)

df_tr &lt;- subset(df_tr, tr_type_simp &gt; 0)
df_rtr &lt;- subset(df_rtr, rtr_type_simp &gt; 0)

df_tr$psych &lt;- c(rep(&#39;PTS&#39;, 4), rep(&#39;PTG&#39;, 4))
df_rtr$psych &lt;- c(rep(&#39;PTS&#39;, 4), rep(&#39;PTG&#39;, 4))

names(df_rtr) &lt;- names(df_tr)

df_tr &lt;- rbind(df_tr, df_rtr)

p_ptsptg &lt;- ggplot(df_tr, aes(x = as.factor(tr_type_simp), y = avg, fill = psych)) +
  theme_bw() +
  facet_wrap(vars(exp)) +
  geom_bar(stat = &#39;identity&#39;, position = position_dodge()) +
  scale_fill_manual(name = &#39;Psychological \nResponse&#39;,
                    values = c(&#39;grey65&#39;, &#39;gray30&#39;)) +
  scale_x_discrete(name = &#39;\nType of Traumatic Event&#39;, 
                   labels = c(&#39;Natural Disaster&#39;, &#39;Anthropogenic \nDisaster&#39;,
                                    &#39;Anthropogenic \nInterpersonal \nAccidental&#39;, 
                                    &#39;Anthropogenic \nInterpersonal \nIntentional&#39;)) +
  ylim(c(0, 1)) +
  ylab(&#39;Mean Posttraumatic Response&#39;) +
  # labs(title = &#39;Prevalence of Exposure to Traumatic Events, by Type&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# ggsave(&#39;p_ptsptg.png&#39;, p_ptsptg, &#39;png&#39;, &#39;~/Dropbox/Wayde/Dissertation/Trust&#39;,
#     width = 190, height = 100, units = &#39;mm&#39;)

df_bl &lt;- datp %&gt;%
  group_by(black) %&gt;%
  summarize(avg = mean(ptsi, na.rm = T))

df_bl2 &lt;- datp %&gt;%
  group_by(black) %&gt;%
  summarize(avg = mean(ptgi, na.rm = T))

df_hi &lt;- datp %&gt;%
  group_by(hisp) %&gt;%
  summarize(avg = mean(ptsi, na.rm = T))

df_hi2 &lt;- datp %&gt;%
  group_by(hisp) %&gt;%
  summarize(avg = mean(ptgi, na.rm = T))

df_fem &lt;- datp %&gt;%
  group_by(female) %&gt;%
  summarize(avg = mean(ptsi, na.rm = T))

df_fem2 &lt;- datp %&gt;%
  group_by(female) %&gt;%
  summarize(avg = mean(ptgi, na.rm = T))

df_bl &lt;- data.frame(
  psych = c(rep(&#39;PTS&#39;, 2), rep(&#39;PTG&#39;, 2)),
  Race = c(&#39;Non-Black&#39;, &#39;Black&#39;, &#39;Non-Black&#39;, &#39;Black&#39;),
  avg = c(0.300, 0.375, 0.497, 0.658)
)

df_hi &lt;- data.frame(
  psych = c(rep(&#39;PTS&#39;, 2), rep(&#39;PTG&#39;, 2)),
  Race = c(&#39;Non-Hispanic&#39;, &#39;Hispanic&#39;, &#39;Non-Hispanic&#39;, &#39;Hispanic&#39;),
  avg = c(0.302, 0.386, 0.503, 0.588)
)

df_fem &lt;- data.frame(
  psych = c(rep(&#39;PTS&#39;, 2), rep(&#39;PTG&#39;, 2)),
  Race = c(&#39;Non-Female&#39;, &#39;Female&#39;, &#39;Non-Female&#39;, &#39;Female&#39;),
  avg = c(0.258, 0.347, 0.468, 0.550)
)

df_demo &lt;- do.call(&#39;rbind&#39;, list(df_bl, df_hi, df_fem))

df_demo$demo &lt;- c(rep(&#39;Race&#39;, 4), rep(&#39;Ethnicity&#39;, 4), rep(&#39;Sex&#39;, 4))

p_demo &lt;- ggplot(df_demo, aes(x = as.factor(psych), y = avg, fill = Race)) +
  theme_bw() +
  facet_wrap(vars(demo)) +
  geom_bar(stat = &#39;identity&#39;, position = position_dodge()) +
  scale_fill_manual(name = &#39;Demographic&#39;,
                    values = c(&#39;gray5&#39;, &#39;gray5&#39;, &#39;gray5&#39;,
                               &#39;grey65&#39;, &#39;grey65&#39;, &#39;grey65&#39;)) +
  # scale_x_discrete(name = &#39;\nType of Traumatic Event&#39;,
  #                  labels = c(&#39;Natural Disaster&#39;, &#39;Anthropogenic \nDisaster&#39;,
  #                                   &#39;Anthropogenic \nInterpersonal \nAccidental&#39;,
  #                                   &#39;Anthropogenic \nInterpersonal \nIntentional&#39;)) +
  xlab(&#39;Psychological Response&#39;) +
  ylim(c(0, 1)) +
  ylab(&#39;Mean Posttraumatic Response&#39;) +
  # labs(title = &#39;Prevalence of Exposure to Traumatic Events, by Type&#39;) +
  theme(axis.text=element_text(size = 10)) + 
  theme(plot.title = element_text(hjust = 0.5)) + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(plot.caption = element_text(hjust = 0)) +
  theme(text=element_text(size = 10, family=&quot;serif&quot;))

# ggsave(&#39;p_demo.png&#39;, p_demo, &#39;png&#39;, &#39;~/Dropbox/Wayde/Dissertation/Trust&#39;,
#     width = 190, height = 100, units = &#39;mm&#39;)</code></pre>
</div>
<div id="tests" class="section level2">
<h2>Tests</h2>
<div id="regression" class="section level3">
<h3>Regression</h3>
<pre class="r"><code># demographics
## income less than 0.3 is lower class, upper class is greater than 0.8 (YouGov scale)
## corresponds to less than $50k a year and more than $120k a year (family income)
datp$class &lt;- ifelse(datp$income &lt; 0.3, 0,
                ifelse(datp$income &gt; 0.8, 1, 0.5))
datp$class2 &lt;- ifelse(datp$income &lt; 0.3, &#39;lower&#39;,
                ifelse(datp$income &gt; 0.8, &#39;upper&#39;, &#39;middle&#39;))
dat1$class &lt;- ifelse(dat1$income &lt; 0.3, 0,
                ifelse(dat1$income &gt; 0.8, 1, 0.5))
dat1$class2 &lt;- ifelse(dat1$income &lt; 0.3, &#39;lower&#39;,
                ifelse(dat1$income &gt; 0.8, &#39;upper&#39;, &#39;middle&#39;))
dat1$lower &lt;- ifelse(dat1$class == 0, 1, 0)

datp2 &lt;- subset(datp, tr_type_simp &gt; 0 | rtr_type_simp &gt; 0)
datp2$lower &lt;- ifelse(datp2$class == 0, 1, 0)
datp$lower &lt;- ifelse(datp$class == 0, 1, 0)

datp$Direct &lt;- ifelse(datp$tr_exp == 1, 1, 0)
datp$Indirect &lt;- ifelse(datp$rtr_exp == 1, 1, 0)

datp$tr_exp_t &lt;-ifelse(is.na(datp$tr_exp_t), 50, datp$tr_exp_t)
datp$rtr_exp_t &lt;-ifelse(is.na(datp$rtr_exp_t), 50, datp$rtr_exp_t)

datp$Time &lt;- ifelse(datp$tr_exp == 1 &amp; datp$rtr_exp != 1, datp$tr_exp_t,
             ifelse(datp$rtr_exp == 1 &amp; datp$tr_exp != 1, datp$rtr_exp_t,
             ifelse(datp$tr_exp == 1 &amp; datp$rtr_exp == 1, pmin(datp$tr_exp_t, datp$rtr_exp_t), NA)))

datp$Time2 &lt;- ifelse(datp$Time == 0, 1,
              ifelse(datp$Time == 1, 4,
              ifelse(datp$Time &lt; 0.5 &amp; datp$Time &gt; 0, 2,
              ifelse(datp$Time &gt; 0.5 &amp; datp$Time &lt; 1, 3, NA))))

# datp$Time &lt;- ifelse(datp$Time == 50 | datp$Time == 50, NA, datp$Time)

datp$PTS &lt;- datp$ptsi
datp$PTG &lt;- datp$ptgi
datp$Black &lt;- datp$black
datp$Female &lt;- datp$female
datp$Class &lt;- datp$class
datp$Republican &lt;- datp$gop
datp$PolInterest &lt;- datp$newsint

datp$rtr_type_simp &lt;- ifelse(is.na(datp$rtr_type_simp), 0, datp$rtr_type_simp)
datp$Type &lt;- ifelse(datp$tr_exp == 1 &amp; datp$rtr_exp != 1, datp$tr_type_simp,
             ifelse(datp$rtr_exp == 1 &amp; datp$tr_exp != 1, datp$rtr_type_simp,
             ifelse((datp$tr_exp == 1 &amp; datp$rtr_exp == 1) &amp; datp$tr_exp_t &gt; datp$rtr_exp_t, datp$rtr_type_simp, 
             ifelse((datp$tr_exp == 1 &amp; datp$rtr_exp == 1) &amp; datp$tr_exp_t &lt;= datp$rtr_exp_t, datp$tr_type_simp, NA))))

# save(datp, file = &#39;datp_final.Rda&#39;)

m_type1 &lt;- feols(ptgi ~ factor(tr_type_simp) + tr_exp_t + black + hisp + female + lower | study,
              weights = ~weight,
              data = subset(datp2, tr_type_simp &gt; 0))</code></pre>
<pre><code>## NOTE: 214 observations removed because of NA values (RHS: 214).</code></pre>
<pre class="r"><code>m_type2 &lt;- feols(ptsi ~ factor(tr_type_simp) + tr_exp_t + black + hisp + female + lower | study,
              weights = ~weight,
              data = subset(datp2, tr_type_simp &gt; 0))</code></pre>
<pre><code>## NOTE: 215 observations removed because of NA values (LHS: 1, RHS: 214).</code></pre>
<pre class="r"><code>m_type1_r &lt;- feols(ptgi ~ factor(rtr_type_simp) + rtr_exp_t + black + hisp +female + lower | study,
              weights = ~weight,
              data = subset(datp2, rtr_type_simp &gt; 0))</code></pre>
<pre><code>## NOTE: 204 observations removed because of NA values (RHS: 204).</code></pre>
<pre class="r"><code>m_type2_r &lt;- feols(ptsi ~ factor(rtr_type_simp) + rtr_exp_t + black + hisp + female + lower | study,
              weights = ~weight,
              data = subset(datp2, rtr_type_simp &gt; 0))</code></pre>
<pre><code>## NOTE: 205 observations removed because of NA values (LHS: 1, RHS: 204).</code></pre>
<pre class="r"><code>summary(m_type1); summary(m_type2); summary(m_type1_r); summary(m_type2_r)</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptgi
## Observations: 2,144 
## Weights: weight 
## Fixed-effects: study: 4
## Standard-errors: Clustered (study) 
##                        Estimate Std. Error   t value  Pr(&gt;|t|)    
## factor(tr_type_simp)2 -0.062587   0.035643 -1.755938 0.1773602    
## factor(tr_type_simp)3  0.010367   0.015536  0.667268 0.5523391    
## factor(tr_type_simp)4 -0.007247   0.004458 -1.625572 0.2025119    
## tr_exp_t              -0.029527   0.010808 -2.732065 0.0718158 .  
## black                  0.173007   0.019807  8.734463 0.0031597 ** 
## hisp                   0.111072   0.034384  3.230335 0.0482036 *  
## female                 0.085236   0.007765 10.976752 0.0016189 ** 
## lower                  0.019019   0.008958  2.123017 0.1238277    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.253168     Adj. R2: 0.083439
##                  Within R2: 0.087259</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptsi
## Observations: 2,143 
## Weights: weight 
## Fixed-effects: study: 4
## Standard-errors: Clustered (study) 
##                        Estimate Std. Error   t value   Pr(&gt;|t|)    
## factor(tr_type_simp)2  0.096990   0.015562  6.232544 0.00832955 ** 
## factor(tr_type_simp)3  0.007722   0.009284  0.831796 0.46654376    
## factor(tr_type_simp)4  0.194274   0.014651 13.260522 0.00092676 ***
## tr_exp_t              -0.160763   0.016419 -9.791256 0.00226403 ** 
## black                  0.051223   0.026839  1.908557 0.15234220    
## hisp                   0.060475   0.027701  2.183099 0.11700847    
## female                 0.038455   0.015406  2.496123 0.08800708 .  
## lower                  0.034538   0.024767  1.394525 0.25748428    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.242113     Adj. R2: 0.193692
##                  Within R2: 0.193265</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptgi
## Observations: 2,175 
## Weights: weight 
## Fixed-effects: study: 4
## Standard-errors: Clustered (study) 
##                         Estimate Std. Error  t value  Pr(&gt;|t|)    
## factor(rtr_type_simp)2  0.055513   0.008826  6.28980 0.0081168 ** 
## factor(rtr_type_simp)3  0.024150   0.011971  2.01729 0.1370149    
## factor(rtr_type_simp)4 -0.024815   0.022650 -1.09556 0.3533440    
## rtr_exp_t              -0.040942   0.028396 -1.44184 0.2450179    
## black                   0.182130   0.026273  6.93232 0.0061549 ** 
## hisp                    0.116054   0.014896  7.79070 0.0044011 ** 
## female                  0.087808   0.013399  6.55327 0.0072250 ** 
## lower                   0.015811   0.003405  4.64385 0.0188237 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.260751     Adj. R2: 0.090387
##                  Within R2: 0.093891</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptsi
## Observations: 2,174 
## Weights: weight 
## Fixed-effects: study: 4
## Standard-errors: Clustered (study) 
##                         Estimate Std. Error   t value   Pr(&gt;|t|)    
## factor(rtr_type_simp)2  0.115689   0.045992   2.51540 0.08652556 .  
## factor(rtr_type_simp)3  0.039444   0.019334   2.04016 0.13402565    
## factor(rtr_type_simp)4  0.095260   0.006801  14.00722 0.00078796 ***
## rtr_exp_t              -0.131065   0.007746 -16.92047 0.00044957 ***
## black                   0.071138   0.021978   3.23675 0.04796924 *  
## hisp                    0.072061   0.017101   4.21374 0.02441992 *  
## female                  0.069900   0.022222   3.14548 0.05144487 .  
## lower                   0.047919   0.013925   3.44120 0.04119993 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.256462     Adj. R2: 0.100171
##                  Within R2: 0.101561</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Direct PTG&#39;  = m_type1,
                           &#39;Direct PTS&#39; = m_type2,
                           &#39;Indirect PTG&#39; = m_type1_r,
                           &#39;Indirect PTS&#39;  = m_type2_r),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre><code>## Warning: To compile a LaTeX document with this table, the following commands must be placed in the document preamble:
## 
## \usepackage{booktabs}
## \usepackage{siunitx}
## \newcolumntype{d}{S[
##     input-open-uncertainty=,
##     input-close-uncertainty=,
##     parse-numbers = false,
##     table-align-text-pre=false,
##     table-align-text-post=false
##  ]}
## 
## To disable `siunitx` and prevent `modelsummary` from wrapping numeric entries in `\num{}`, call:
## 
## options(&quot;modelsummary_format_numeric_latex&quot; = &quot;plain&quot;)
##  This warning appears once per session.</code></pre>
<pre class="r"><code>m_demo1 &lt;- feols(ptsi ~ black + hisp + female + lower,
                 data = dat1)</code></pre>
<pre><code>## NOTE: 310 observations removed because of NA values (LHS: 288, RHS: 47).</code></pre>
<pre class="r"><code>m_demo2 &lt;- feols(ptgi ~ black + hisp + female + lower,
                 data = dat1)</code></pre>
<pre><code>## NOTE: 309 observations removed because of NA values (LHS: 286, RHS: 47).</code></pre>
<pre class="r"><code>m_demo3 &lt;- feols(ptsi ~ black + hisp + female + lower | study,
                 weights = ~weight,
                 data = datp2)</code></pre>
<pre><code>## NOTE: 261 observations removed because of NA values (LHS: 1, RHS: 260).</code></pre>
<pre class="r"><code>m_demo4 &lt;- feols(ptgi ~ black + hisp + female + lower | study,
                 weights = ~weight,
                 data = datp2)</code></pre>
<pre><code>## NOTE: 260 observations removed because of NA values (RHS: 260).</code></pre>
<pre class="r"><code>summary(m_demo1); summary(m_demo2)</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptsi
## Observations: 1,245 
## Standard-errors: IID 
##             Estimate Std. Error   t value   Pr(&gt;|t|)    
## (Intercept) 0.242538   0.013563 17.882523  &lt; 2.2e-16 ***
## black       0.012726   0.022498  0.565650 5.7173e-01    
## hisp        0.080561   0.033379  2.413551 1.5942e-02 *  
## female      0.106668   0.015609  6.833984 1.2918e-11 ***
## lower       0.058593   0.015776  3.714144 2.1296e-04 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.273713   Adj. R2: 0.04897</code></pre>
<pre><code>## OLS estimation, Dep. Var.: ptgi
## Observations: 1,246 
## Standard-errors: IID 
##             Estimate Std. Error   t value   Pr(&gt;|t|)    
## (Intercept) 0.362969   0.013681 26.530481  &lt; 2.2e-16 ***
## black       0.175376   0.022698  7.726591 2.2673e-14 ***
## hisp        0.028058   0.033678  0.833120 4.0494e-01    
## female      0.081370   0.015741  5.169375 2.7352e-07 ***
## lower       0.002000   0.015908  0.125717 8.9998e-01    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## RMSE: 0.276176   Adj. R2: 0.06239</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Study 1 PTG&#39;  = m_demo2,
                           &#39;Study 1 PTS&#39; = m_demo1,
                           &#39;Studies 2-5 PTG&#39;  = m_demo4,
                           &#39;Studies 2-5 PTS&#39; = m_demo3),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># m_trst1 &lt;- feols(trstgov ~ ptsi + black + female + income | study,
#                  weights = ~weight,
#               data = subset(datp, tr_type_simp &gt; 0))
# summary(m_trst1)


## MAIN ANALYSES ####

m_trst &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = datp)</code></pre>
<pre><code>## NOTE: 1,293 observations removed because of NA values (RHS: 1,293).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.072959   0.044172  1.651719 0.197159    
## PTG          0.046430   0.032724  1.418823 0.250999    
## Time        -0.057116   0.016105 -3.546534 0.038185 *  
## Black        0.060892   0.015316  3.975622 0.028460 *  
## Female      -0.035311   0.009458 -3.733640 0.033491 *  
## Class        0.061338   0.026260  2.335777 0.101612    
## Republican  -0.251838   0.045273 -5.562630 0.011462 *  
## PolInterest  0.003191   0.028354  0.112556 0.917492    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.018947   0.047067 -0.402551 0.7142459    
## PTG          0.056664   0.007310  7.751701 0.0044653 ** 
## Time        -0.048856   0.018540 -2.635139 0.0779803 .  
## Black        0.021538   0.014599  1.475277 0.2366027    
## Female      -0.028127   0.022704 -1.238843 0.3034971    
## Class        0.067175   0.024611  2.729523 0.0719696 .  
## Republican  -0.086504   0.029243 -2.958158 0.0596321 .  
## PolInterest  0.012942   0.038617  0.335143 0.7595841    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.070100   0.038784 -1.807436 0.168422    
## PTG          0.047167   0.010012  4.710899 0.018107 *  
## Time        -0.030865   0.018758 -1.645409 0.198436    
## Black       -0.000891   0.018927 -0.047062 0.965422    
## Female      -0.031612   0.015734 -2.009157 0.138096    
## Class        0.082106   0.012960  6.335261 0.007953 ** 
## Republican  -0.082310   0.033730 -2.440249 0.092480 .  
## PolInterest  0.079550   0.030385  2.618060 0.079133 .  
## ---
## Dep. var.: trstgen
##              Estimate Std. Error  t value  Pr(&gt;|t|)    
## PTS         -0.167287   0.036474 -4.58647 0.0194666 *  
## PTG          0.037454   0.035593  1.05229 0.3699424    
## Time        -0.116922   0.015800 -7.40021 0.0051039 ** 
## Black       -0.104429   0.041521 -2.51509 0.0865490 .  
## Female      -0.069657   0.036028 -1.93341 0.1486678    
## Class        0.158440   0.041739  3.79598 0.0320922 *  
## Republican  -0.100622   0.029401 -3.42239 0.0417698 *  
## PolInterest  0.110647   0.031893  3.46927 0.0403678 *  
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error    t value   Pr(&gt;|t|)    
## PTS         -0.227778   0.011272 -20.207230 0.00026493 ***
## PTG          0.101894   0.029476   3.456892 0.04073203 *  
## Time        -0.044383   0.023750  -1.868741 0.15845096    
## Black       -0.078160   0.007302 -10.703583 0.00174342 ** 
## Female      -0.008918   0.017328  -0.514670 0.64230076    
## Class        0.120676   0.023909   5.047238 0.01500045 *  
## Republican  -0.068713   0.025086  -2.739118 0.07139121 .  
## PolInterest  0.087200   0.026991   3.230670 0.04819128 *</code></pre>
<pre class="r"><code>m_trst_direct &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Direct == 1))</code></pre>
<pre><code>## NOTE: 250 observations removed because of NA values (RHS: 250).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst_direct)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error    t value  Pr(&gt;|t|)    
## PTS          0.077715   0.052811   1.471557 0.2375232    
## PTG          0.051037   0.032219   1.584045 0.2113509    
## Time        -0.079467   0.007192 -11.048766 0.0015881 ** 
## Black        0.067049   0.012826   5.227360 0.0136199 *  
## Female      -0.036621   0.008873  -4.127415 0.0257934 *  
## Class        0.077789   0.031236   2.490397 0.0884530 .  
## Republican  -0.262423   0.047996  -5.467541 0.0120259 *  
## PolInterest  0.005839   0.052523   0.111167 0.9185041    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.014916   0.057921 -0.257532 0.813422    
## PTG          0.048148   0.008260  5.829163 0.010057 *  
## Time        -0.070852   0.009963 -7.111277 0.005722 ** 
## Black        0.030805   0.013542  2.274654 0.107463    
## Female      -0.022566   0.019901 -1.133914 0.339246    
## Class        0.078496   0.031034  2.529340 0.085474 .  
## Republican  -0.084689   0.034252 -2.472487 0.089866 .  
## PolInterest  0.037264   0.054667  0.681642 0.544372    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.065104   0.054755 -1.189014 0.3199709    
## PTG          0.043655   0.026971  1.618585 0.2039696    
## Time        -0.046669   0.022819 -2.045190 0.1333791    
## Black        0.003624   0.014159  0.255929 0.8145508    
## Female      -0.024029   0.016241 -1.479562 0.2355474    
## Class        0.098868   0.013040  7.582130 0.0047594 ** 
## Republican  -0.099113   0.038309 -2.587229 0.0812689 .  
## PolInterest  0.096838   0.048758  1.986092 0.1412188    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error  t value  Pr(&gt;|t|)    
## PTS         -0.157431   0.028687 -5.48793 0.0119021 *  
## PTG          0.032072   0.031826  1.00772 0.3878242    
## Time        -0.112562   0.012869 -8.74665 0.0031469 ** 
## Black       -0.076243   0.040786 -1.86936 0.1583544    
## Female      -0.059955   0.051971 -1.15362 0.3322210    
## Class        0.145197   0.047741  3.04136 0.0558073 .  
## Republican  -0.108548   0.022060 -4.92053 0.0160826 *  
## PolInterest  0.133624   0.071691  1.86390 0.1592136    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error    t value  Pr(&gt;|t|)    
## PTS         -0.215644   0.018760 -11.494759 0.0014134 ** 
## PTG          0.096252   0.049146   1.958491 0.1450657    
## Time        -0.034250   0.025927  -1.320991 0.2782268    
## Black       -0.063658   0.007709  -8.257551 0.0037192 ** 
## Female      -0.006051   0.016578  -0.365015 0.7393152    
## Class        0.124947   0.029113   4.291843 0.0232575 *  
## Republican  -0.070620   0.021402  -3.299648 0.0457455 *  
## PolInterest  0.103405   0.032139   3.217427 0.0486796 *</code></pre>
<pre class="r"><code>m_trst_indirect &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Indirect == 1))</code></pre>
<pre><code>## NOTE: 249 observations removed because of NA values (RHS: 249).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst_indirect)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.058264   0.040427  1.441223 0.245176    
## PTG          0.049980   0.023419  2.134204 0.122523    
## Time        -0.058538   0.014794 -3.956808 0.028815 *  
## Black        0.070421   0.016777  4.197387 0.024673 *  
## Female      -0.042912   0.014954 -2.869684 0.064067 .  
## Class        0.062379   0.023044  2.706938 0.073354 .  
## Republican  -0.237048   0.042332 -5.599718 0.011252 *  
## PolInterest -0.010466   0.032906 -0.318073 0.771285    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.033068   0.049053 -0.674122 0.548529    
## PTG          0.072913   0.019433  3.752036 0.033070 *  
## Time        -0.046750   0.016482 -2.836488 0.065837 .  
## Black        0.016467   0.005556  2.963832 0.059361 .  
## Female      -0.042698   0.032932 -1.296557 0.285505    
## Class        0.070198   0.034934  2.009486 0.138052    
## Republican  -0.081985   0.018750 -4.372444 0.022132 *  
## PolInterest -0.003623   0.049203 -0.073641 0.945931    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.072377   0.027579 -2.624333 0.0787075 .  
## PTG          0.070198   0.006641 10.570906 0.0018085 ** 
## Time        -0.020203   0.016532 -1.222058 0.3089471    
## Black       -0.008339   0.023145 -0.360300 0.7424964    
## Female      -0.040441   0.022639 -1.786350 0.1720176    
## Class        0.079935   0.018471  4.327580 0.0227495 *  
## Republican  -0.070983   0.031728 -2.237248 0.1112452    
## PolInterest  0.061762   0.030809  2.004674 0.1386965    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.148252   0.038646 -3.836116 0.0312312 *  
## PTG          0.030359   0.031431  0.965889 0.4053489    
## Time        -0.116097   0.017443 -6.655586 0.0069135 ** 
## Black       -0.094761   0.048534 -1.952455 0.1459233    
## Female      -0.077679   0.029716 -2.614071 0.0794058 .  
## Class        0.158965   0.044330  3.585923 0.0371302 *  
## Republican  -0.111743   0.039515 -2.827879 0.0663056 .  
## PolInterest  0.083471   0.044277  1.885183 0.1558944    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error    t value   Pr(&gt;|t|)    
## PTS         -0.231429   0.027869  -8.304126 0.00365905 ** 
## PTG          0.109907   0.023211   4.735112 0.01785666 *  
## Time        -0.036163   0.018573  -1.947084 0.14669143    
## Black       -0.082420   0.005687 -14.492732 0.00071224 ***
## Female      -0.008060   0.022076  -0.365101 0.73925761    
## Class        0.108111   0.017947   6.023920 0.00916943 ** 
## Republican  -0.067497   0.019901  -3.391719 0.04272075 *  
## PolInterest  0.082003   0.028826   2.844777 0.06538908 .</code></pre>
<pre class="r"><code>m_vote &lt;- feols(c(presvote16, presvote20) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = datp)</code></pre>
<pre><code>## NOTE: 1,293 observations removed because of NA values (RHS: 1,293).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_vote)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: presvote16
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.022947   0.037381 -0.613881 0.5827072    
## PTG          0.035822   0.041825  0.856475 0.4546976    
## Time         0.130571   0.023877  5.468431 0.0120205 *  
## Black        0.048038   0.049411  0.972216 0.4026513    
## Female      -0.004592   0.021232 -0.216256 0.8426584    
## Class        0.125985   0.011411 11.040622 0.0015915 ** 
## Republican   0.061188   0.026076  2.346492 0.1006260    
## PolInterest  0.409437   0.057001  7.182938 0.0055598 ** 
## ---
## Dep. var.: presvote20
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.094968   0.026000 -3.652650 0.0354266 *  
## PTG          0.040927   0.020083  2.037836 0.1343261    
## Time         0.085339   0.029532  2.889765 0.0630251 .  
## Black       -0.040730   0.043767 -0.930606 0.4207030    
## Female      -0.004628   0.015068 -0.307165 0.7788053    
## Class        0.078541   0.017847  4.400685 0.0217542 *  
## Republican   0.011059   0.038803  0.285015 0.7941765    
## PolInterest  0.357967   0.037011  9.671980 0.0023467 **</code></pre>
<pre class="r"><code>m_vote_direct &lt;- feols(c(presvote16, presvote20) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Direct == 1))</code></pre>
<pre><code>## NOTE: 250 observations removed because of NA values (RHS: 250).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_vote_direct)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: presvote16
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.032996   0.036433 -0.905680 0.43186911    
## PTG         -0.005690   0.056915 -0.099976 0.92666959    
## Time         0.134234   0.025379  5.289130 0.01318480 *  
## Black        0.042480   0.058985  0.720181 0.52345424    
## Female      -0.008632   0.030388 -0.284044 0.79485303    
## Class        0.127689   0.007111 17.955449 0.00037675 ***
## Republican   0.058934   0.005979  9.856979 0.00222012 ** 
## PolInterest  0.390620   0.065908  5.926711 0.00959883 ** 
## ---
## Dep. var.: presvote20
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.096597   0.022420 -4.308589 0.0230176 *  
## PTG          0.014352   0.036870  0.389265 0.7230671    
## Time         0.081520   0.023517  3.466446 0.0404505 *  
## Black       -0.054264   0.057368 -0.945890 0.4139872    
## Female      -0.011396   0.016977 -0.671275 0.5501090    
## Class        0.077076   0.025627  3.007633 0.0573195 .  
## Republican   0.008077   0.035690  0.226310 0.8355030    
## PolInterest  0.371693   0.031184 11.919300 0.0012701 **</code></pre>
<pre class="r"><code>m_vote_indirect &lt;- feols(c(presvote16, presvote20) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Indirect == 1))</code></pre>
<pre><code>## NOTE: 249 observations removed because of NA values (RHS: 249).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_vote_indirect)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: presvote16
##             Estimate Std. Error  t value  Pr(&gt;|t|)    
## PTS         0.004576   0.029364 0.155843 0.8860527    
## PTG         0.036003   0.031810 1.131821 0.3400014    
## Time        0.127229   0.017092 7.443927 0.0050182 ** 
## Black       0.028508   0.046741 0.609908 0.5850152    
## Female      0.011313   0.032444 0.348690 0.7503585    
## Class       0.127834   0.018602 6.872116 0.0063102 ** 
## Republican  0.067659   0.024428 2.769716 0.0695850 .  
## PolInterest 0.390760   0.060628 6.445238 0.0075743 ** 
## ---
## Dep. var.: presvote20
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.121294   0.033100 -3.664457 0.0351356 *  
## PTG          0.059385   0.020510  2.895466 0.0627332 .  
## Time         0.071851   0.032809  2.189983 0.1162563    
## Black       -0.064136   0.040715 -1.575234 0.2132812    
## Female       0.014136   0.015223  0.928577 0.4216018    
## Class        0.082773   0.023322  3.549217 0.0381120 *  
## Republican   0.008190   0.036765  0.222773 0.8380181    
## PolInterest  0.327616   0.053494  6.124351 0.0087519 **</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst[1],
                           &#39;State&#39;  = m_trst[2],
                           &#39;Local&#39;  = m_trst[3],
                           &#39;Interpersonal I&#39;  = m_trst[4],
                           &#39;Interpersonal II&#39;  = m_trst[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst_direct[1],
                           &#39;Federal&#39;  = m_trst_indirect[1],
                           &#39;State&#39;  = m_trst_direct[2],
                           &#39;State&#39;  = m_trst_indirect[2],
                           &#39;Local&#39;  = m_trst_direct[3],
                           &#39;Local&#39;  = m_trst_indirect[3],
                           &#39;Interpersonal I&#39;  = m_trst_direct[4],
                           &#39;Interpersonal I&#39;  = m_trst_indirect[4],
                           &#39;Interpersonal II&#39;  = m_trst_direct[5],
                           &#39;Interpersonal II&#39;  = m_trst_indirect[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;2016 Voting&#39;  = m_vote[1],
                           &#39;2020 Voting&#39;  = m_vote[2]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;2016 Voting&#39;  = m_vote_direct[1],
                           &#39;2016 Voting&#39;  = m_vote_indirect[1],
                           &#39;2020 Voting&#39;  = m_vote_direct[2],
                           &#39;2020 Voting&#39;  = m_vote_indirect[2]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># all
coefplot(m_trst,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;,
                   par(family = &#39;serif&#39;))
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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ADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggAABUPoAAggggAACCCCAAAIIIIAAAggggAACCDgrQADU2aalYggggAACCCCAAAIIIIAAAggggAACCCBAAJQ+gAACCCCAAAIIIIAAAggggAACCCCAAALOChAAdbZpqRgCCCCAAAIIIIAAAggggAACCCCAAAIIEAClDyCAAAIIIIAAAggggAACCCCAAAIIIICAswIEQJ1tWiqGAAIIIIAAAggggAACCCCAAAIIIIAAAgRA6QMIIIAAAggggAACCCCAAAIIIIAAAggg4KwAAVBnm5aKIYAAAggggAACCCCAAAIIIIAAAggggEBDCBCIpsD27dvlqaeekvLy8mielnMhgAACCCCAAAIIIIAAAggggAACVgo0b95c7rzzTklLS7Oy/PFQaAKg8dAKCVQGDX7+6le/SqAaU1UEEEAAAQQQQAABBBBAAAEEEEAgPIGzzz5bJk2aFF4mCXw0AdAEbvxYVN278nPcuHFy4YUXxqIInBMBBBBAAAEEEEAAAQQQQAABBBCwQuDll1+W5cuXcydtmK1FADRMQA4PTUCDn7fffntoB3MUAggggAACCCCAAAIIIIAAAgggkAACGvzUH1J4AiyCFJ4fRyOAAAIIIIAAAggggAACCCCAAAIIIIBAHAsQAI3jxqFoCCCAAAIIIIAAAggggAACCCCAAAIIIBCeAAHQ8Pw4GgEEEEAAAQQQQAABBBBAAAEEEEAAAQTiWIAAaBw3DkULT6CgoEAWLVokR44cCS8jjkYgygKlpaXy8ccfy/bt26N8Zk6HQPgCq1atkmXLloWfETkgEGWBwsJCM244fPhwlM/M6RAIT6CsrMyMG7Zu3RpeRhyNQAwE1qxZI5999pmcPHkyBmfnlAiELrB7924zbigqKgo9E46MqgAB0Khyc7JoCmgA9MCBA7Jv375onpZzIRC2wMGDB02/zc/PDzsvMkAg2gIauNe+W15eHu1Tcz4EwhLwxg179+4NKx8ORiDaAocOHWLcEG10zuebgI4b9P1XA/kkBGwS2LVrl4k3MG6wp9UIgNrTVpQUAQQQQAABBBBAAAEEEEAAAQQQQAABBIIUIAAaJBi7I4AAAggggAACCCCAAAIIIIAAAggkrkC9evUSt/KW1ryhpeWm2AjUKdCxY0cz/2fr1q3r3JcdEIgngRYtWkibNm0kIyMjnopFWRAISKBLly6i89gmJSUFtD87IRAvAjpuKC4uNu+/8VImyoFAIALNmzc3/TYzMzOQ3dkHgbgS0HGDrtnQqFGjuCoXhUGgLoH09HTRqcvatm1b1668HicCBEDjpCEohv8C+oakPyQEbBNITk6WYcOG2VZsyouAEejduzcSCFgp0K5dO9EfEgK2CWjgiHGDba1GeT2BXr16eQ/ZImCVgAY+CX5a1WTCLfB2tRelRQABBBBAAAEEEEAAAQQQQAABBBBAAIEgBAiABoHFrggggAACCCCAAAIIIIAAAggggAACCCBglwABULvai9IigAACCCCAAAIIIIAAAggggAACCCCAQBACBECDwGJXBBBAAAEEEEAAAQQQQAABBBBAAAEEELBLgACoXe1FaYMQOHnypBw9ejSII9gVgfgROHbsmFRUVMRPgSgJAgEKHD9+XMrKygLcm90QiB8Bxg3x0xaUJHgBxg3Bm3FEfAgwboiPdqAUoQkQbwjNLVZHEQCNlTznjbjA+vXrZf78+bJnz56In4sTIOCnQFFRkcybN09yc3P9zJa8EIiKwKJFi2ThwoUE8KOizUn8FMjLyzPjhsLCQj+zJS8EIi5QXFxsxg0rV66M+Lk4AQJ+CyxevFgWLFggJ06c8Dtr8kMgogKbNm0y44aCgoKInofM/RMgAOqfJTnFmYD3bYy3jbPiURwEzijg9Vlve8YdeQGBOBTQfqtXgPJBJg4bhyLVKuC953rbWnfmRQTiSECv/tRE342jRqEoAQuUlJRIeXm56JWgJARsEtC+q4n3XntajQCoJW3l/eeypLgUEwEEEEAAAQQQQAABBBBAAAEEEEAAgbgQIAAaF81QdyFWr14tl1xyiTz55JNy4MCBug9gD6lf/z/du0GDBmggYJWA13e9rVWFp7AJL+D1W2+b8CAAWCPg9Vlva03BKWjCC3hjXfpuwncFKwG8futtrawEhU5IAa/PetuERLCs0g0tK6/1xdXL+//5z3/KF198IYcOHZJzzjlH+vXrJ126dKm1bueff77MmjVLBg4cKDNmzJAJEybISy+9VOsxif5ijx49pFmzZpKenp7oFNTfMoE2bdpInz59RLeup7179542V6TeypecnCz16tWrVv2UlBRp2rRptef4Jf4E9O+U3sbmfSCPvxJSIgRqFsjJyZG0tDTJyMioeQeeRSBOBVq2bCl9+/aVVq1axWkJKRYCZxYYMGCAlJaWSlJS0pl34hUE4lAgOztb9PNJZmZmHJaOItUkQAC0JpUIPbdixQq5/vrra1zYZOLEifLzn/9cunfvfsazt27dWsaPHy+//vWvZfbs2QRAzyj1nxdSU1OlW7dudezFywjEn4AG/rp27Rp/BfO5RD/84Q/loYceCjjXhg0bykcffSRDhgwJ+Bh2jL5A27Zto39SzoiADwL6Iaa2cZgPpyALBCIioOOGrKysiORNpghEWiARvvCPtCH5x0agcePGjBtiQx/yWQmAhkwX3IGff/65+dCuV4DWlP785z/LnDlz5IEHHjBXeNa0jz7H1YxnkuF5BBCwTUCvfO/QoUO1xXL0ysH9+/ebqwf1S5+qSa/o1qtcSAgggAACCCCAAAIIIIAAAggEI0AANBitEPfVoKde+ekFP/Xy/ilTppjb2fVy6Y0bN4oGSOfNmyczZ840t8f/4Q9/MLeAnnpKbg04VYTfEUDAVoGbbrpJ9KdqWrdunfTq1Ut0Cou1a9dWfYnHCCCAAAIIIIAAAggggAACCIQkQAA0JLbgDvLm/NSj9Gqn+fPnS+/evSszGT16tHl89OhRc3v7ww8/LKNGjZLXX3+dq50qlXiAAAIIIIAAAggggAACCCCAAAIIIIBA8AKsAh+8WdBH6AruXnr22WerBT+953Wrc0/96Ec/MleE6nwSI0aMkIKCgqq78BgBBBBAAAEEEEAAAQQQQAABBBBAAAEEghAgABoEVqi75ufnm0N1/rqLL764zmx0ns+5c+fKt771Lbngggtk8+bNdR7DDqcLlJSUGLsTJ06c/iLPIBDHAidPnpQvv/xSiouL47iUFA2BmgV2794tO3furPlFnkUgjgX0TpxNmzaJzkVMQsAmAR03bNmyRYqKimwqNmVFwAjs2bNHvM/LkCBgkwDjBpta6z9lJQAahTbTuew06arOukpjIKl+/fqit8LrKslf+9rXZM2aNYEcxj5VBPLy8kSvvuUq2iooPLRCYO/evbJq1SrmwLSitSjkqQLLly+XpUuXVlvc6tR9+B2BeBTQOdl1vEUAPx5bhzLVJqCLB+bm5vJ5oTYkXotbgRUrVsiyZcsq18uI24JSMAROEdAL1XTcsGPHjlNe4dd4FWAO0Ci0zJAhQ0QDmjqw1qsRGzRoEPBZdbEkXShp7Nix8re//S3g49hRpKKiwjB4W0wQsEXA67Pe1pZyU04EVMDrt7oN5u8degjEWqBq3411WTg/AsEI0HeD0WLfeBOg/8Zbi1CeQAXou4FKxc9+XAEahbbQK0Cvuuoq0VuyFy5cGPQZdZGkV199VSZOnCiffvpp0MdzAAIIIIAAAggggAACCCCAAAIIIIAAAokqQAA0Si3/0EMPiQZCJ0+eLLt27Qr6rOeff7689dZb8tFHHwV9bKIekJqaaqrubRPVgXrbJ6ALommi79rXdpT4P/22UaNGXP1JZ7BOwHvP9bbWVYACJ6yAjht0mi36bsJ2Aasrrv02KSlJGjbk5lSrGzIBC++953rbBCSwrsq8y0SpyTIyMszVm9/97ndl0KBB8vjjj8ull15qbo0PtAhnnXWWCYCOHDmShZECQOvZs6dkZWVJ48aNA9ibXRCIHwFdMG3MmDGiQSQSArYJDB8+XHRBDp36hYSATQI5OTnSuXNnxg02NRplNQJNmjQRvWNMg0gkBGwTGDZsmBk3MG2ObS1HebOzs810hcQb7OkLfDqJYlu1aNHCrO6uc3kuXrxYxo8fH/TZNaCnV4H26tUr6GMT7QD9Jpw3o0RrdXfqm5ycHPCiae7Umpq4IKBXcPAh3IWWTLw6MG5IvDZ3qcY6buCLJ5daNHHqwrghcdraxZoSb7CrVbkCNAbtpVeA6k+oqUOHDvLFF1+EejjHIYAAAggggAACCCCAAAIIIIAAAgggkDACXAFqaVMzR4qlDUexEUAAAQQQQAABBBBAAAEEEEAAAQSiKkAANKrc4Z/sscceM/NaTp06NfzMyAEBBBBAAAEEEEAAAQQQQAABBBBAAAHHBbgF3qIGPnjwoMycOVPKysrk+eefl6uvvlouuOCCiNfghRdekOeee85MTh3uyTZt2mSyWLhwofTr10900vb+/fvXONdhXl6eFBQUmP11Xq4uXbqYn1PLcPz4cVm+fLkcO3bMvKQTaPft21eaNm166q6yb98+Wbt2rVRUVJjXOD/+9L/4/P9XWloqH374ofl/yv9/3v94/+fvn/aBUxN//xn/MP5j/Mv4n88/+reBz398/k2kz/+njof4PXABAqCBW8V8T10ZWleT37Jli1lgonv37lEp07x588wK9n6ebP369aIB3eLiYjlx4oTUdEu/Bj91Hy+lpKTUGADVQMmuXbu83cy2qKhIDh8+LBs3bpQBAwZIWlqaef7AgQOyf//+yn0jef6aArCcH/9A+p/2af0CQP9veCnY/m97/9PARjj//22vf7jvf7Gs/+rVq83fqIEDB3rd12x5/+P9L5D3P+0sser/hYWFsmHDBtH3Hx0feCnR3n9j5e95c/7Qx7+bN2+W8847z6Os3PL+y/tvPL//rlmzRo4cOWLWyOD/f+j///XzbyzHf5w/ev6Vb+48CFqg3sl/p6CP4oCYCfzrX/+SV199VUaOHFnjACcSBTt69KjoHybvqslwznHNNdeYwM63vvUtefnll80q7frBoqZUXl5ugpjeaxoA1m/3akolJSWiQSNNuo/uu2LFClEvvdK0c+fO5jXt7vrm7NVFV22L1PnNCU/5h/PjH0j/0w/h//znP6Vly5Zy7rnnml4UbP8/peuZX23of+vWrZNevXrJWWedJUuWLKmsRqLU36twuO9/Xj5Vt9Fq/48++sgEkPR9vupq8NE6v/aVmhLn5/23rvfflStXyrZt26R3797SqlWrym7E+09w469KuCoP+P8X2f9/+oVhbm6utGnTRoYNG1ZF/j8P8Y+svyp7nz9Ow//3E/jX7j937lxzh+OYMWOkfv36YX3+w/90Afpf7f2vqliw42+NkezcuVNycnLM55eqeXmP/fLXOIrGT2bPnm3uBPbyZxucAAHQ4LzYO0wBvd34888/l3HjxsmcOXPCzK32w2sKgNZ+BK8iEB8CXgC0Xbt2MnTo0PgoVJRK4QVAzz77bDNdRZROy2l8FHj77bdrDID6eAqyQiAiAl4AVG+jy8rKisg5yBSBSAjs2bPHfGl4pgBoJM5Jngj4JVA1AJqcnOxXtuSDQMQF9IsnvTtXL1jp1q1bRM9HANQfXhZB8seRXBBAAAEEEEAAAQQQQAABBBBAAAEEEkBA1ykg2SXAHKB2tRelDUKgY8eOZj6Z1q1bB3EUuyIQe4EWLVqY29h0zl8SArYJ6GI1OiVJ1dvfbasD5U1MAR036NyfehUdCQGbBJo3b276bWZmpk3FTsiyvvHGGzJ16lTRW22rJp2iQ4Mpp87hqFdE/vGPf5SvfvWrVXd36rGOG3QO0EaNGjlVLyrjvkB6erpZs6Bt27buV9aRGhIAdaQhqcbpAvqGpD8kBGwT0MFuTXN42VYPypuYAjp/IgkBGwV02hH9ISFgm4AGjhg32NFqu3fvPm3x1qol37dvX9VfTVC06gJG1V505Bed+52EgI0CGvgk+GlXyxEAjWF76cI977zzjmzatEl27Ngh+fn5ZoEevWJRrz7Q+af02z5dxfxMi//EsPicGgEEEEAAAQQQQAABBBBAIECBG264QSZNmlTtClBdcLZTp05mcVj9TFg16ZfiTZo0qfoUjxFAAAEEQhQgABoiXDiHvf/++/LCCy+YRYAOHz5cZ1a6+ujll18uM2bMkB49etS5PzsggAACCCCAAAIIIIAAAgjEn4B+tquaNACqSW+BZ+quqjI8RgABBPwVYBEkfz1rzU1veRg/frx84xvfkNmzZ0sgwU/NUOeEmTVrluiqyNOmTTOr69Z6Il5EAAEEEEAAAQQQQAABBBBAAAEEEEAAASPAFaBR6gh79+6VESNGyIYNG8wZO3fuLOeee67k5OSYWx5SU1NFfxo3biz6LaBOwq8/GvzUYz777DNzi/yzzz4rmzdvlrfeeouJoqPUdpwGAQQQQAABBBBAAAEEEEAAAQQQQMBeAQKgUWq7KVOmmLk+v//974vO/dKnT5+gz7xz5075xS9+IU8//bQ89dRTMn369KDzSKQDTp48KceOHZOUlJREqjZ1dURA+64ualC/PhfqO9KkCVON48ePS0VFBV/SJUyLu1NRxg3utGUi1oRxQyK2uht1ZtzgRjsmai304jXiDfa0Pp+so9BW69atk3nz5skHH3wgv/nNb0IKfmoxO3bsKE8++aTMnTtXHnzwQfMBMwrFt/YU69evl/nz58uePXusrQMFT0wBvfJb3zNyc3MTE4BaWy2waNEiWbhwIX+jrG7FxCx8Xl6eGTcUFhYmJgC1tlZA7xrTccPKlSutrQMFT1yBxYsXy4IFC+TEiROJi0DNrRTQxaw13lBQUGBl+ROx0ARAo9Dqn376qdx0000yfPhwX842atQos0r8li1bfMnP1Uy8CcW9rav1pF7uCXh91tu6V0Nq5LKA9tuysjI+yLjcyI7WzXvP9baOVpNqOSigV39qou862LgJUKWSkhIpLy9nnYsEaGvXqqh9VxPvvfa0bNwEQF2OmuviR+ecc46vvWLw4MGyY8cOX/MkMwQQQAABBBBAAAEEEEAAAQQQQAABBFwTiIsAqK6G/tvf/tY128r6ZGZmyvLlyyt/D/eBzlGlt7j07ds33KycPt6bO7FBgwZO15PKuSfg9V1v614NqZHLAl6/9bYu15W6uSXg9Vlv61btqI3LAt5Yl77rciu7Wzev33pbd2tKzVwT8Pqst3Wtfi7WJy4WQXrllVdEg3quJr1ac/LkyXLRRRfJuHHjwq7m/fffL0lJSdKiRYuw83I5gx49ekizZs0kPT3d5WpSNwcF2rRpY+YK1i0JAdsEBg4caG5j8z6Q21Z+ypu4Ajk5OZKWliYZGRmJi0DNrRRo2bKluTCiVatWVpafQie2wIABA6S0tNR8vk1sCWpvm0B2drZZAEkveCPZIRB0AFQneb3rrrt8q92+ffvMrdz33nuvb3nGW0bdu3eXiy++WC699FKZMGGCmQ/0wgsvlIYNA+fXSaHff/99eeSRR8xEu88++2y8VTPuypOamirdunWLu3JRIATqEqhXr5507dq1rt14HYG4FGjbtm1clotCIVCXgK7iqmM2EgK2Cei4ISsry7ZiU14EjABf+NMRbBVo3Lgx4wbLGi/wCNz/V0xvu9aViV2+YjMSbfjcc8/J6tWr5bXXXjM/zZs3N/OC6rcGOmDRKw40YKeD7+PHj8uRI0dEJ9XNz88XXUV+1apVsn//flO0e+65R6ZOnRqJYpInAggggAACCCCAAAIIIIAAAggggAACTgkEHQBt3769DBkyRD755BNJTk6WDh06SKi3uWkQVRcI0jlAXU/6zdYHH3xgrv5866235NChQ7JkyRLzE2jdNTg6ffp0efDBBwM9hP0QQAABBBBAAAEEEEAAAQQQQAABBBBIaIGgA6CqNXbsWCkrK5OlS5eK3nIRTtIg6IwZM8LJwppjNVj85ptvyrvvviuzZ88WDYQWFxfXWX69nfDmm2+WadOmCbcW1snFDggggAACCCCAAAIIIIAAAggggAACCFQKhBQAHTNmjLldO9zgp5ZC87jjjjvkmWeeqSyU6w/UT3+OHTsmCxYskI0bN0pBQYHs2rVLioqKzFW1nTt3Fv3p1KmTDBo0yNwa77oL9UMAAQQQQAABBBBAAAEEEEAAAQQQQMBvgZACoP369ZPy8nJfyqJBv9atW8ukSZN8yc+mTHTSXL2alhQZAZ1DVQPLOsdqqNM0RKZk5IpA7QJ6ZfyWLVvMFd9NmzatfWdeRSDOBHRqG53LumPHjnFWMoqDQO0CR48eNXOv67ghmIUqa8+VVxGIvICOG7Zu3Wo+UzVr1izyJ+QMCPgosGfPHnN3aUZGho+5khUCkRdg3BB5Y7/PUD+UDPWqzcGDB4dy6GnHLF68WL773e9Kz549T3uNJxAIRyAvL88sPKVBUBICNgns3bvXLHy2du1am4pNWREwAsuXLzdT5Jw4cQIRBKwS0Dty1qxZIzt37rSq3BQWAV0oVRep1f5LQsA2gRUrVsiyZct8u8DKtvpTXnsFNm/ebN53d+zYYW8lEqzkIQVA/TRq0qSJ/PWvf5X33nvPz2zJCwGpqKgwCt4WEgRsEfD6rLe1pdyUEwEV8Pqtt0UFAVsEvD7rbW0pN+VEwOuz3hYRBGwS8Pqtt7Wp7JQ1sQW8PuttE1vDjtqHdAt8XVXTK+50kR+9CkSvZCotLa3xkCNHjpj5L/VFXRxo9OjRNe7HkwgggAACCCCAAAIIIIAAAggggAACCCCAQCgCvgdA//Wvf8nw4cNl27ZtQZXnwgsvDGp/dkagLoHU1FSzi7eta39eRyBeBFJSUkxR6Lvx0iKUIxgB7be6yB9zLwejxr7xIOC953rbeCgTZUAgEAEdN+gUZfTdQLTYJ94EtN/qPLbMvRxvLUN56hLw3nO9bV3783rsBXwPgI4fP94EP9u2bSutWrUyb2RffvmldOvWrVpt9U1O51rq1auX3HjjjaLHkRDwU0DnldWFDHSxKRICNgnoAgZjxoyRRo0a2VRsyoqAEdAvQfVvfP36MZ9lhxZBICiBnJwc6dy5M+OGoNTYOR4EdEoxvZMuKSkpHopDGRAISmDYsGFm3MAXp0GxsXMcCGRnZ0tmZibjhjhoi0CL4GsAVG9p14Vn/vGPf0jVKzqvuOIK+elPfyo9evSoVi69WvS6666Ta665hitFqsnwix8C+k04wU8/JMkjFgLJycmxOC3nRCBsAa7gCJuQDGIkwLghRvCc1hcBxg2+MJJJDAQYN8QAnVP6JkC8wTfKqGTk6+UZX3zxhYwbN65a8FNrccMNN8gTTzxxWoU6deokQ4cOlZtuuum013gCAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIV8DXAKiuftW+ffvTyvT1r39dFi9eLPv27TvttVGjRsmf/vQnWbZs2Wmv8QQCCCCAAAIIIIAAAggggAACCCCAAAIIIBCOgK8B0K5du5rb308tkN5SNHnyZLn88sulrKys2st6y7yml19+udrz/IIAAggggAACCCCAAAIIIIAAAggggAACCIQr4GsAtGPHjnLo0CH52te+Jj/72c/Mbe+HDx82ZdTb3Dds2GAWO1qyZIkJhC5atEgeeOAB8/rWrVvDrQvHI4AAAggggAACCCCAAAIIIIAAAggggAAC1QR8XQRJr/R86KGH5LLLLqu8ElRXgNf5P1NSUuTnP/+5XH/99fL3v//drA6rt8x7SVfsJiHgp0BBQYFs3LhRBgwYIGlpaX5mTV4IRFSgtLRUli5dalYj1hWJSQjYJLBq1SrRPjxw4ECbik1ZEZDCwkLzZX3//v1FV9UmIWCLgN5h99lnn5nViLOysmwpNuVEwAisWbNGdDHlQYMGicYTSAjYIrB7925Zv369nHfeedKsWTNbip3Q5fT1ClCVvPTSS+Xpp5+WRo0amTewCy64oBJYb4N/5JFHzO9Vg59t27aV6dOnV+7HAwT8ENAA6IEDB2qce9aP/MkDgUgJHDx40PTb/Pz8SJ2CfBGImMD27dtF+255eXnEzkHGCERCwBs37N27NxLZkycCERPQO/B0rQXGDREjJuMICui4Qd9/T50qL4KnJGsEfBHYtWuXiTcwbvCFMyqZ+HoFqFfiadOmydVXXy1FRUXmm0jved3+93//t/Tu3VvefPNNWblypZxzzjly77331rh4UtXjeIwAAggggAACCCCAAAIIIIAAAggggAACCAQrEJEAqBZCLwE+02XAo0ePFv0hIYAAAggggAACCCCAAAIIIIAAAgggYJMAUzbY1Fr/KavvAdAtW7aIrgZPQiDWArool84n07p161gXhfMjEJRAixYtpE2bNpKRkRHUceyMQDwIdOnSxcwBmpSUFA/FoQwIBCyg44bi4mLz/hvwQeyIQBwING/e3PTbzMzMOCgNRUAgOAEdN+hnNp1Cj4SATQLp6emiU5fplI4kOwR8DYCeOHHCzAG6bNkyadjQ16zt0KSUcSWgb0j6Q0LANoHk5GQZNmyYbcWmvAgYAZ3mhoSAjQLt2rUT/SEhYJuABo4YN9jWapTXE+jVq5f3kC0CVglo4JPgp1VNJr4vgvTFF1+YOT5/+9vfSklJiV0alBYBBBBAAAEEEEAAAQQQQAABBBBAAAEEnBLwPQCqOnobxv333y96K9HNN98subm5TqFRGQQQQAABBBBAAAEEEEAAAQQQQAABBBCwQ8D3AOiVV14pn376qezYsUNef/11sxL8kCFDZOjQofLCCy/I0aNH7ZChlAgggAACCCCAAAIIIIAAAggggAACCCBgvYCvAdAGDRrIo48+alB0RayvfvWrMnv2bMnPz5errrpKHnvsMXNV6C233CKrV6+2Ho8KIIAAAggggAACCCCAAAIIIIAAAggggEB8C/gaANWqtm/f/rQat2zZUr7//e+Lzg86b948szrsV77yFTNZ94svvshVoaeJ8YQfAidPnqRv+QFJHjEROHbsmFRUVMTk3JwUgXAEjh8/LmVlZeFkwbEIxESAcUNM2DmpTwKMG3yCJJuoCzBuiDo5J/RRgDucfcSMQla+B0DrKrPOC6o/TZs2lSVLlsjkyZMlIyPDXB1a17G8jkAwAuvXr5f58+fLnj17gjmMfRGIuUBRUZH5soj5k2PeFBQgBIFFixbJwoULCeCHYMchsRXIy8sz44bCwsLYFoSzIxCkQHFxsRk3rFy5Msgj2R2B2AssXrxYFixYICdOnIh9YSgBAkEIbNq0yYwbCgoKgjiKXWMp0DAaJ9ermObOnSvPP/+8vPPOO9Xe3Fq1aiXf+973ZPz48dEoCudIIAHv2xhvm0BVp6qWC3h91ttaXh2Kn2AC2m/1ag79IFO/ftS/Z00wbarrp4D3nutt/cybvBCIpIBe/amJvhtJZfKOlEBJSYmUl5ebsYNOqUdCwBYB7buaeO+1pcVEfA2A6oedn/3sZ/KTn/zECOzcuVN+//vfy+9+9zvZvn17NZUBAwaYFeKvuOIKSUlJqfYavyCAAAIIIIAAAggggAACCCCAAAIIIIAAAn4I+BoA1QLpVZ4a0NRL2d9++23zTY5X0OTkZJk4caIJfA4ePNh7mi0CERHwrjzim8SI8JJpBAW8vuttI3gqskbAdwGv33pb309AhghESMDrs942QqchWwR8F/DGuvRd32nJMAoCXr/1tlE4JadAwBcBr896W18yJZOICvgeANWrPu+6665qhe7SpYtMnTpVpkyZIm3btq32Gr8gECmBHj16SLNmzSQ9PT1SpyBfBCIi0KZNG+nTp4/oloSAbQIDBw7kNjbbGo3yGoGcnBxJS0szc9NDgoBNArrgbN++fUWnFiMhYJuA3hlaWloqSUlJthWd8ia4QHZ2trn4LzMzM8El7Km+7wFQr+r16tWTkSNHmqs9x44dK943k97rbBGItEBqaqp069Yt0qchfwR8F9D3z65du/qeLxkiEA0BvuiMhjLniISA3sHUvXv3SGRNnghEVEDHDVlZWRE9B5kjECkBvvCPlCz5RlqgcePGjBsijexz/hEJgI4aNUp+/etfS+/evX0uLtkhgAACCCCAAAIIIIAAAggggAACCCCAAAKBC/geAB00aJBZ6Z0rPgNvBPZEAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgI1PczW5389fHHH+d2dz9RyQsBBBBAAAEEEEAAAQQQQAABBBBAAAEEQhbwNQCq888UFBTIhAkTZO7cuSEXigMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAE/BHwNgBYVFcnVV18tf/nLX+S2227zo3zkgUDIAiUlJbJ582Y5ceJEyHlwIAKxEDh58qR8+eWXUlxcHIvTc04EwhLYvXu37Ny5M6w8OBiBWAgcPXpUNm3aJMePH4/F6TknAiEL6Lhhy5Ytop/FSAjYJrBnzx7Jz8+3rdiUFwFh3GBfJ/B1DtCKigrRq0A1XXLJJfZpUGKnBPLy8mTbtm2SnJwsmZmZTtWNyrgtsHfvXlm1apWkp6fL4MGD3a4stXNOYPny5VJWViZjx45lShznWtftCm3cuNEEkRo1aiSdO3d2u7LUzimB/fv3S25urrRr106GDh1qfd20PqdewKB/V5KSkio/a3qVTElJkSZNmni/srVQYMWKFXLs2DHTf7WNSQjYIqAXW+lPw4YNJSsry5ZiJ3Q5fb0CtEWLFjJp0iRp3Lix3H333QHDHjlyRJ566qmA92dHBAIR0IC8Jm8byDHsg0A8CHh91tvGQ5koAwKBCnj91tsGehz7IRBrAa/PettYlydS57/sssvMF2z6JZv306pVK/MBrmXLlpXPea+NGTMmUkUhX58EvD7rbX3KNibZ/OQnP5HWrVubYJgGdL0fvZihffv2lb97z2ufXbJkSUzKykn9EfD6rbf1J1dyQSDyAl6f9baRPyNnCFfA1ytAtTCPPvqoiYIvXbpURo8eHVD5NmzYIHrFEwkBBBBAAAEEEEAAAQQiJ7Bu3TopLCys8QQHDx487fn169ebL5N1sVMSApEW8AKdVQMKOi3FgQMHzF0FGqyvmpo2bSp6EQ4JAQQQQACBugR8DYDqHyedd3HWrFkybdo0ee211+TGG2+UU/9QeYXSP2xbt26Ve+65R7797W97T7NFwBeB1NRUk4+39SVTMkEgCgJ6O5cm+m4UsDmF7wLab/VWtgYNGvieNxkiEEkB7z3X20byXLHMW2+V1luMq6YnnnhCHnroIbn11lvlhz/8YdWXRK+wI/hZjSTuftFxg05D5kLf1c+O+lM16fQUPXr0kG7duolOcUVyS0D7rc5jq7cRkxCwScB7z/W2NpU9Ucvq67vMoUOHJDs723zw8UB///vfew9r3RIArZWHF0MQ6Nmzp5mLQ6dkICFgk0CzZs1EbznUeehICNgmMHz4cPNBhoCJbS1HeXNycszcn66PG3SOPb2VuGry5lBMS0s77bWq+/E4PgW0/fTOO+ZPjM/2oVS1CwwbNsyMG/jitHYnXo0/AY196VXrro8b4k8+9BL5ei+LztcyceLE0EvDkQj4KKDfhPNm5CMoWUVVQBfv8haVi+qJORkCYQroFRx8CA8TkcNjIsC4ISbsnNQnAR038MWTT5hkE1UBxg1R5eZkPgsQb/AZNMLZ+XoFqJZ18uTJ8tJLL0nXrl3Nbe0aFK3tcnad+/NPf/pThKtJ9ggggAACCCCAAAIIIIAAAggggAACCCCQiAK+B0AvvPBC6dKli7zzzjty1llnBWT6zW9+U3TRJBICCCCAAAIIIIAAAggggAACCCCAAAIIIOCngK+3wGvB9NaL2267zUxUHWhBhw4desaFkgLNg/0QQAABBBBAAAEEEEAAAQQQQAABBBBAAIFTBXwPgOoJfvCDH9Q6B41e7VlUVFRZFl0lXledJCGAAAIIIIAAAggggAACCCCAAAIIIIAAAn4KRCQAqgXUFeEffvhhueqqq+Suu+6qVuajR49Kv3795JFHHqn2PL8g4KdAQUGBLFq0SI4cOeJntuSFQMQFSktL5eOPP5bt27dH/FycAAG/BVatWiXLli3zO1vyQyDiAoWFhWbccPjw4YifixMg4KdAWVmZGTds3brVz2zJC4GoCKxZs0Y+++wzsxJ8VE7ISRDwSWD37t1m3FD14j6fsiabCAlEJAC6cOFC6d27t8ycOVNeeeUVycvLq1b8ESNGyNy5c+X55583AVL9sE9CwG8BDYAeOHBA9u3b53fW5IdARAUOHjxo+m1+fn5Ez0PmCERCQAP32nfLy8sjkT15IhAxAW/coAt0khCwSUAvPNHxLuMGm1qNsnoCOm7Q918N5JMQsElg165dJt7AuMGeVvN9ESR9Axs3bpzU9e15jx495MMPP5Ts7Gxp3769PProo/aoUVIEEEAAAQQQQAABBBBAAAEEEEAAAQQQsELA9ytAp0+fboKfqampMmrUKJk4ceIZITp27GiuAH3iiSfMZe9n3JEXEEAAAQQQQAABBBBAAAEEEEAAAQQQiAOBevXqxUEpKEIwAr4HQBcsWCD9+/eXHTt2yLx58+TKK6+stTx6q3xFRYW5Vb7WHXkRgSAFNMCuC2y1bt06yCPZHYHYCrRo0ULatGkjGRkZsS0IZ0cgBIEuXbpIZmamJCUlhXA0hyAQOwFv3KDvvyQEbBJo3ry5GTfoey8JAdsEdNyg77+NGjWyreiUN8EF0tPTTbyhbdu2CS5hT/V9vQV+586dZvGjp556Slq2bBmQgs7RqCk3Nzeg/dkJgUAF9A1Jf0gI2CaQnJwsw4YNs63YlBcBI6BfbJIQsFGgXbt2oj8kBGwT0MAR4wbbWo3yegK9evXyHrJFwCoBDXwS/LSqycTXK0B10JiSkiJ9+vQJWGHJkiVmX130g4QAAggggAACCCCAAAIIIIAAAggggAACCPgp4GsAtGHDhtKvXz9ZvXp1QGV8/fXX5b333jP7Dhw4MKBj2AkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhUwNcAqJ708ssvl/vuu0+OHTtWaxneeOMNmTx5cuU+Q4YMqXzMAwQQQAABBBBAAAEEEEAAAQQQQAABBBBAwA8BX+cA1QLdfvvt8tFHH8mIESNk6tSp4q2MVVxcLBs2bJB169bJc889J96t73rMyJEj5dprr9WHJAQQQAABBBBAAAEEEEAAAQQQQAABBBBAwDcB3wOgGvB88cUXZcKECTJlyhRTUL01vlmzZjUWWleM/8tf/iK6DwkBPwVOnjxprkTWeWlJCNgmoFfR66IG9ev7fqG+bRSU1zKB48ePS0VFBau5WtZuFFeEcQO9wGYBxg02t15il51xQ2K3v+21P3r0qFkHx/Z6JEr5I/LJumnTpvLuu+/K3LlzpW/fvqJvaqemnj17yssvvyxLly6V5s2bn/oyvyMQtsD69etl/vz5smfPnrDzIgMEoilQVFQk8+bNk9zc3GielnMh4IvAokWLZOHChSYI6kuGZIJAlATy8vLMuKGwsDBKZ+Q0CPgjoHfa6bhh5cqV/mRILghEUWDx4sWyYMECOXHiRBTPyqkQCF9g06ZNZtxQUFAQfmbkEBWBiF52OWbMGNEf/aOswaitW7dKhw4dpEePHqIrxpMQiKSAfhujydtG8lzkjYCfAl6f9bZ+5k1eCERaQPutfvGpH2S4gjnS2uTvp4D3nutt/cybvBCIpIC39gJ9N5LK5B0pgZKSEikvLzdjhwYNGkTqNOSLgO8C2nc18d7rO23EMvQ9AOoFOZOTkysLrVeEDho0yPxUPvn/D2699VZ58MEHz3iL/Kn78zsCCCCAAAI2COjVZE888cRpd0GsXbvW3B6enZ1drRqNGzeWmTNnmi8Kq73ALwgggAACCCCAAAIIIIAAAmEJ+B4AHTp0qMyZM0cCXdX9mmuukUsvvdTcLq/z3ZEQ8EvAu/KIbxL9EiWfaAl4fdfbRuu8nMdfgZepGfQXAABAAElEQVReekmeeeaZM2b6/vvvn/aaTg8zbdq005636Qmv33pbm8pOWRNbwOuz3jaxNai9TQLeWJe+a1OrUVZPwOu33tZ7ni0C8S7g9VlvG+/lpXwivgdAg0UdMGCA6LwfujK8Xg1KQsAvAZ1qQRffSk9P9ytL8kEgKgJt2rSRPn36iG5J9grccccd0r17d3Nbl1cLvU3xtttuE71L4sknn/SeNltdsE0XELQ9DRw4kNvYbG/EBC1/Tk6OpKWlSUZGRoIKUG1bBVq2bGnWXWjVqpWtVaDcCSyg8YDS0lJJSkpKYAWqbqOA3s2l4/fMzEwbi5+QZY5pAPTAgQPy9NNPmzc8XTCBAGhC9sGIVTo1NVW6desWsfzJGIFICdSrV0+6du0aqezJN0oCLVq0kOuvv77a2XRObA2A6h0PN954Y7XXXPmlbdu2rlSFeiSYgH6I0S8tSAjYJqDjhqysLNuKTXkRMAJ84U9HsFVAp69i3GBX64UdAJ00aZL89a9/ray1Lnrw9a9/XRo2/E/W+ge5ph+9CubIkSOVx7ESfCUFDxBAAAEEEEAAAQQQQAABBBBAAAEEEEDAJ4GwA6CvvvqqWeThzjvvrLzNL9hVsPR2o+9973s+VYlsEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB/wiEHQDVbPR2Pp3za+zYsXLw4EHp169fnau660SxnTp1Ep1vafz48XL22WfTJggggAACCCCAAAIIIIAAAggggAACCCCAgK8CvgRAtUTDhg2Tv//97zJq1Ciz6m2gq8D7WhsyQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEqgjUr/I47IcaBH3jjTekQ4cOYedFBgiEK1BSUiKbN28WnZeWhIBNAidPnpQvv/xSdMEcEgK2CezevVt27txpW7EpLwKiUzht2rRJjh8/jgYCVgnouGHLli1SVFRkVbkpLAIqsGfPHsnPzwcDAesEGDdY12TiawBUqz9y5Ejp0qVLUBLl5eWyYMGCoI5hZwTqEsjLy5PVq1dLQUFBXbvyOgJxJbB3715ZtWqVrF27Nq7KRWEQCERg+fLlsnTpUr58CgSLfeJKYOPGjbJmzRoC+HHVKhQmEIH9+/dLbm6u6b+B7M8+CMSTwIoVK2TZsmWV64nEU9koCwK1CejFVjpu2LFjR2278VocCfgeAA2lbvPnz5ePP/44lEM5BoEzClRUVJjXvO0Zd+QFBOJMwOuz3jbOikdxEKhVwOu33rbWnXkRgTgS8Pqst42jolEUBGoV8Pqst611Z15EIM4EvH7rbeOseBQHgTMKeH3W255xR16IGwHf5gCtWiPtAJ988omsW7dONmzYYG4pqvp61ce6jwY/77777qpP8xgBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhbwPcAqM7hceWVV3JLe9hNQwbhCqSmpposvG24+XE8AtESSElJMaei70ZLnPP4KaD99tixY9KgQQM/syUvBCIu4L3netuIn5ATIOCTgI4b6tWrJ/Rdn0DJJqoC2m91HtuGDX0PTUS1Hpws8QS891xvm3gC9tXY93eZq6++muCnff3AyRL37NlTsrKypHHjxk7Wj0q5K9CsWTMZM2aMNGrUyN1KUjNnBYYPH24+yNSvHxez7DjrTMX8F8jJyZHOnTszbvCflhwjLNCkSRMZPXq0JCUlRfhMZI+A/wK6kLIGQPni1H9bcoysQHZ2tmRmZjJuiCyzr7n7GgA9ePCgvP/++6aAkyZNknHjxsngwYOladOmNRZaV9nUlWJ/9KMf1fg6TyIQjoB+E07wMxxBjo2lQHJycixPz7kRCFmAKzhCpuPAGAswbohxA3D6sAQYN4TFx8ExFGDcEEN8Th22APGGsAmjmoGvAdCysjLR+T/PP/98+d///d+AKpKeni4PPfSQzJkzJ6D92QkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhUwNcAaLt27Uzws2vXroGe3+zXt29fCfaYoE7AzggggAACCCCAAAIIIIAAAggggAACCCCQkAK+T9Clt76/9957ore3B5M2bdoUzO7siwACCCCAAAIIIIAAAggggAACCCCAAAII1CngewD0lltukd69e8usWbPqPLm3w+rVq+XNN9/0fmWLAAIIIIAAAggggAACCCCAAAIIIIAAAgj4IuDrLfA6B+jGjRvlgQcekCuuuMJcBaq3xdeWtm3bJrNnzzYLJtW2H68hEKxAQUGB6Y8DBgyQtLS0YA9nfwRiJlBaWipLly41qxHrisQkBGwSWLVqlWgfHjhwoE3FpqwISGFhoWzYsEH69+8vuqo2CQFbBPQz2GeffWZWI87KyrKl2JQTASOwZs0aOXLkiAwaNEh0MToSArYI7N69W9avXy/nnXeeNGvWzJZiJ3Q5fQ2AHj161Awa9Y+wpptvvjlgXF0xnhS4wIIFC2TkyJGBH5CAe2oA9MCBA7Jv3z4CoAnY/jZX+eDBg6bfNmjQwARBba4LZU88ge3bt5svQHV+76SkpMQDoMbWCnjjhr179xIAtbYVE7Pghw4dMuMGDR5lEQBNzE5gca113KDxA/1JTk62uCYUPdEEdu3aZeINOm4gAGpH6/t6C3zz5s3lm9/8ph01t7yUGgAlIYAAAggggAACCCCAAAII2C9w8uRJ+ytBDRBAAIE4FvD1ClCt5/jx4+Xtt9+Wyy+/3KwIn5KScsbq65u8RstffvnlM+7DCzUL/POf/6z5BZ5FAAEEEEAAAQQQQAABBBCwQuDw4cOmnDp9THl5OXdPWNFqFBIBYcoGCzuB7wHQSy65RLp16yavvPJKwG/ew4cPl48//thCvsCKvGTJEvnb3/4W2M517FVRUSG5ubny4Ycf1rEnL3fs2NHMJ9O6dWswELBKoEWLFtKmTRvJyMiwqtwUFgEV6NKli5kDlNvf6Q+2Cei4obi42Lz/2lZ2ypvYAnoXno4bMjMzExvC0tr/8pe/NCXXi4N+85vfyPTp0y2tSWjF1nGDzgHaqFGj0DLgKARiJJCeni46dVnbtm1jVAJOG6yA7wHQVq1ayVNPPRVw8FML/PWvf130A7+rSefiefrpp+XYsWOuVjEu66VvSPpDQsA2AZ3/aNiwYbYVm/IiYAR69+6NBAJWCujCnXUt3mllxSi08wIaOGLcYGczb9myRZ588snKwv/0pz+Va665JqECKr169aqsPw8QsElAA58EP21qMRFf5wD1qn7RRRd5DwPa1q9f3+nVYvWKgqlTpwZkwU4IIIAAAggggAACCCCAAALuC9xxxx1m8R+tqX4m1gWt7rnnHvcrTg0RQACBGAiEdAXotddeKy+++KJvcx5s2rTJ3DL/k5/8JAYE0TnlzJkz5be//a1MmDBBbrzxRklNTQ3pxCUlJWa6gB/+8IchHc9BCCCAAAIIIIAAAggggAACsRX44IMPzDRpaWlp5hZwnTpGpzv7/e9/L9OmTZPzzjsvtgXk7AgggIBjAiEFQI8ePSrbtm0TvbXbj7RhwwZxfdW79u3by8033yyjRo2Sr3zlK2Gx6fHvv/9+WHlwMAIIIIAAAggggAACCCCAQPQFTpw4Ibfffrs58V133SX33nuvuQJUPy8++uij5rV//OMf0S8YZ0QAAQQcFgj5FvgHHnhA9GrEcNOBAweqzXsSbn7xfLz+cVu6dKkvRTznnHN8yYdMEEAAAQQQQAABBBBAAAEEoiegdwbqwrZdu3aVW2+9tfLEekekzimoC96+9tprlc/zAAEEEEAgfIGQrgDV086aNUteeOEF8wadkpISUkkOHz4s+/btM1d/hntVZEgFiPJBujrjyJEjfTnrD37wA1/ycTkTvapYF54KtX+6bEPd4l9A+64uaqDzQZEQsEng+PHj5hY+VnO1qdUoqwowbqAf2CzAuMGe1tNVo72p3x555BHRxS+91Lx5c/nZz35mpky788475Tvf+Y40btzYe9nJLeMGJ5s1YSqld0cTb7CnuUMOgGoVdY6SwsJCe2obByUdNGiQL6XIzMz0JR+XM1m/fr3k5eWZKQdYnc3llnavbkVFRaLzQnXp0oX5n9xrXudrtGjRIiktLZXRo0cTwHe+td2qoI4ZdOwwZMgQ0amLSAjYIlBcXGymx+rUqZP079/flmInbDnvu+8+2bt3r3zta1+Tyy67TDSAUjVNmTJFnnnmGVm5cqVogPRHP/pR1Zede7x48WIzB6qOGxo0aOBc/aiQuwK6ls2aNWvk/PPPlw4dOrhbUYdqxqVFDjUmVaku4A0mvG31V/kNgfgV8Pqst43fklIyBE4X0H5bVlYmOr8ZCQGbBLz3XG9rU9kpa2IL6NWfmui78d8P9EuWp59+2gT6Hn/88RoLrHf/eK/94he/kPz8/Br3c+VJnVavvLxc9EpQEgI2CXhTQvLea0+rhXwFqF5Rp99O6RVKoV7yq7fAb9y40ax0Zw8ZJUUAAQQQQAABBBBAAAEEEEAgOAGdxkwDfVOnTpU+ffqc8eALL7xQJkyYYOYBnTFjhrz88stn3JcXEEAAAQQCEwg5APriiy/KRRddFNhZ6tirc+fOordukBDwU8CbO5FbKfxUJa9oCHh919tG45ycAwG/BLx+6239ypd8EIi0gNdnvW2kz0f+CPgl4I116bt+iUYmn7ffflveffddadGiheiCwnWlX/3qV/LWW2/JK6+8Iro6/NChQ+s6xMrXvX7rba2sBIVOSAGvz3rbhESwrNIh3wI/YsQI36o6fPhw3/JyPaPHHntMsrKyzLeGrtc13Pr16NFDzj33XElPTw83K45HIKoCumCaXhXQq1evqJ6XkyHgh8DAgQPNXEjeB3I/8iQPBKIhkJOTI71795aMjIxonI5zIOCbQMuWLaVv375yzjnn+JYnGfkroLd4T58+3WSqc4DqWK+upHda3nHHHWaBtttuu81s6zrGxtcHDBggOnZISkqysfiUOYEFsrOzzbiB9Vns6QQhXQF67bXXSlpamm+1POuss8xq3b5l6GhGumLgzJkzzdxqzz//vFx99dVywQUXRLy2r732mvzud78zi16FezKdKFjTjh07ZMmSJdKkSRMTpKxXr95pWX/55Zeya9euyud1EFDThxKdZ04nCddFNzTph279AKN9tFu3bpXH6wM1XLduXeUAItLnr3Zyzo9/gP1P/z/oasSrVq2q7EKh9P/Kg///gU39X+eQ1PcIL7lS/2XLlpkq6ftWbm5uRN//PDtva1P7+/H+79Xb21J//v7V9fdfp3Tq3r270P/CH395/++8Lf//ovP/70yLftru7/Wjqtvaxv9V99PH8VD/H//4x2ZxVr3zUS/+0XFeIJ9/9HP3H/7wB1m6dKm89NJLct1115nq2Vb/2t5/qwaDef/l/Tfcz/+x+P+vi5p58YdonP/Uc/B74AIhBUC/853vBH6GAPZs2rSp+UMQwK4JvUuzZs1MAHDLli3mGzIdpEcj/fnPf5b58+f7eqoDBw7Inj17ZN++feYqt4YNT++K27dvl0OHDlWeVwObNQVAddJhDahWTbpfTUH63bt3i/54ifPjr1dZ0v/i7/+fXimh7xFecuX/vw6QNFVUVMi2bdt4/+P/H+8//P333uYqt4x/GP8x/q0+/m/VqlXl/w/vgU3jfw3+zZo1yxR98uTJZlEjvdMn0PHnww8/bC58ufvuu2X8+PHmAhKb6s/nLz5/8vnb3/iD9z7INniBev/+9ulk8IdxRKwE/vWvf8mrr74qI0eOlPPOOy8qxSgqKjLfOvrRVXTC782bN8vo0aPNt5ipqanmj3hNFdErOr0BoH5DqvPlnOnWCC2jtwKmDib0VqCavlXVoMP+/fsrr2bl/PjrVcA1JfpfbP7/6YcEDUr37NlTFi1aZJrGpf//Gtjo2rWr+YJGv+Gm//H/j/ef0wV4/43+++/Pf/5zueeee0QDLPfeey/jr///At6lvz82j3+3bt0qOrWVTlORl5dX+aZhy/j/hhtuMHfT6ee3P/7xj3Lq5w8NZupzehW6XiRy6ucf/WwzbNgw+eSTT8zdgA899JAxsKX+fP7i86fN7z/xEn+45pprzGJos2fPNl+IVL4R8iAoAQKgQXGxc7gC/fv3l88//1zGjRsnc+bMCTc7jkcAAccEvADo2WefLWvXrnWsdmIW/NOr+fXOB/3gQkIAAQTiQaBqAFQfkxCIJ4GNGzfWGACNpzKeqSz6uUfnt9Qg4G9+8xtTj1P31S99Lr74YmnUqJFZ9OjU1/V3nUJHv6RITk4246NTp/mq6RieQwABdwQIgPrTlqff9+hPvuSCAAIIIIAAAggggAACCCCAQMIKPPLII5V3nt1yyy21Ouj853qXXG1Jg6VPPPGE+altP15DAAEEEDhdgADo6SY844hASUmJFBQUSFZWllkYyZFqUY0EENDpJnSuX13MQK8UJCFgk4DO83T8+HHp2LGjTcWmrAiI3oaan59vxg01zc0HEQLxKqDjBr1NvHXr1qJ3GZDiR2DSpEnVpt+qqWS6oNHChQulfv36ZpqzmvbxntP3Jr2TzqWkc75r8LemtR5cqid1cU+AcYN9bUoA1L42o8QBCugcQbrIiN4qkpmZGeBR7IZA7AV0oRxdAT49PV0GDx4c+wJRAgSCEFi+fLn5IDN27Fi+fArCjV1jL6C32eqXT3obqq7UTELAFgGdXy83N1fatWsnQ4cOtaXYCVFOvbVdf2pL3hyg+pll3rx5te3q5GsrVqwwazlo/z3TfItOVpxKWS+ga5voj34xoRddkeJfgABoDNtIr1B85513ZNOmTWYVc73qQOeE029v27RpY/4TffWrX5UBAwbwITKEdtK5djR52xCy4BAEYiLg9VlvG5NCcFIEQhTw+q1uGzRoEGIuHIZA9AWq9t3on50zIhC6AH03dDuOjL0A/Tf2bUAJQhOg74bmFsujCIDGQP/999+XF154wSwCdPjw4TpLoLeyXH755TJjxowaJ86uMwN2QAABBBBAAAEEEEAAAQQQQAABBBBAIEEF6sei3kuXLk3I1W91XrTx48fLN77xDZk9e7YEEvzU9tGrQmfNmiW6KvK0adPM3GqxaDfbzpmammqK7G1tKz/lTVyBlJQU+m7iNr/1Ndf3XL2FmKs/rW/KhKuAN17wtgkHQIWtFdBxQ7169YS+a20TJnTBtd/qre/MvZzQ3cDKynvvud7WykokWKEjdgXooUOH5LnnnjPz0eiExr/85S8raXWek379+sl//dd/yR133FH5vMsPdE6/ESNGyIYNG0w1dW6pc889V3JycqRTp05mwKL/cRo3bmwm4S8uLhb90eCnHvPZZ5+ZifmfffZZM8/EW2+9ZT5gumwWbt169uxpphFQUxICNgnoVd9jxozh/7hNjUZZKwWGDx8uuiCHLuZAQsAmAR2T6fiMcYNNrUZZVaBJkyZm9XDmT6Q/2CgwbNgwM27gi1MbWy+xy5ydnW3WGmHcYE8/iEgAVFexmzx5spnXUikuueSSaiIaCJw7d658+9vfls8//9xc3aiTPrucpkyZYub6/P73vy833HCD9OnTJ+jq7ty5U37xi1/I008/LU899ZRMnz496DwS6QD9Jpw3o0Rqcbfq6vp7olutRW2qCnAFR1UNHtskwLjBptairKcKMG44VYTfbRFg3GBLS1HOmgSIN9SkEr/P+R4A3b59u4wbN67O27t79OghH374oWjUvH379vLoo4/Gr1KYJVu3bp1Z0e+DDz4QvTIm1NSxY0d58sknRVfWveKKK+T222/nCptQMTkOAQQQQAABBBBAAAEEEEAAAQQQQCAhBHy/P02vStS5LfV27lGjRsnEiRPPCKkBvauuukqeeOIJc4v3GXe0/IVPP/1UbrrpprCCn1UJ1FVXid+yZUvVp3mMAAIIIIAAAggggAACCCCAAAIIIIAAAqcI+B4AXbBggfTv39/c/j5v3jy58sorTzll9V979+4tFRUV8sorr1R/waHfdPGjc845x9caDR48uHKKAV8zJjMEEEAAAQQQQAABBBBAAAEEEEAAAQQcEvA1AKpzVOriRzo/ZcuWLQNiOnDggNkvNzc3oP1t3CkzM1OWL1/uW9F1cYmVK1dK3759fcuTjBBAAAEEEEAAAQQQQAABBBBAAAEEEHBRwNcAaLt27SQlJSWoBX6WLFliXA8ePOiir6mTXq35hz/8QV5//XVf6nj//feLrvLYokULX/JzNZOCggJZtGiRHDlyxNUqUi9HBUpLS+Xjjz8WnVOZhIBtAqtWrZJly5bZVmzKi4AUFhaacYNO5URCwCaBsrIyM27YunWrTcWmrAgYgTVr1pjp8PQiHxICNgnonb4abygqKrKp2AldVl8DoLqCW79+/WT16tUBoWpA8L333jP7Dhw4MKBjbNype/fucvHFF8ull15q5kRduHChHD9+PKiqnDhxwliNHj1aNAB64403BnV8Iu6sAVC9wnjfvn2JWH3qbLGAfiGk/TY/P9/iWlD0RBXQwL323fLy8kQloN6WCnjjhr1791paA4qdqAJ6Bx7jhkRtffvrreMGff/VQD4JAZsEdu3aZeINjBvsaTXfV4G//PLL5b777pM5c+ZI48aNzyjxxhtvyOTJkytfHzJkSOVjFx8899xzJjD82muvif40b97czAuanZ0tWVlZkpaWZhaO0itoNTiqVy2WlJSYD5G6irxeUbN//35Dc88998jUqVNdZKJOCCCAAAIIIIAAAggggAACCCCAAAII+CrgewD09ttvl48++khGjBhhgnT16tUzBS4uLpYNGzaIBvM0GOjd+q4vjhw5Uq699lpfKxZvmemq7R988IFZDf6tt94yc6WqQVWHusqswdHp06fLgw8+WNeuvI4AAggggAACCCCAAAIIIIAAAgggEAEBL9YVgazJMkICvgdAtRO8+OKLMmHCBJkyZYoptt4a36xZsxqroCvG/+UvfxHdx/XUoUMHefPNN+Xdd9+V2bNniwZCNTBcV2rbtq3cfPPNMm3aNNHHpMAEOnbsaK6kbd26dWAHsBcCcSKg8/vqlyYZGRlxUiKKgUDgAl26dBGdx1bnqiYhYJOAjht0XKbvvyQEbBLQO8u03+rCqyQEbBPQcYPe/dioUSPbik55E1wgPT1ddOoyYjT2dISIRB2bNm1qgnwa6Js5c6Z88cUXp4n07NlTfvzjH8t3v/tdqV/f16lITztXvD0xZswY0Z9jx47JggULZOPGjWbeE51DQifQ1UBp586dzU+nTp1k0KBBZnGpeKtHvJdH35D0h4SAbQLJyckybNgw24pNeREwAr1790YCASsFdDFP/SEhYJuABo4YN9jWapTXE+jVq5f3kC0CVglo4JPgp1VNJhEJgHoEXqBPv01fv3696MqEGtzr0aMHA8x/I+kcqWPHjvW42CKAAAIIIIAAAggggECYAkePHj0tB11huqbbFfVLx0S7GOM0HJ5AAAEEEEAgAQQiGgD1/PSKUL2KUX806ZWPJAQQQAABBBBAAAEEEEDAT4GLL77YTDMVaJ56t5BeqKG3kduSysvLRQO6gSSdZowAbyBS7IMAAggg4LpARO4937Rpk5mzcsaMGXLixInTDJctWybXXHON7Nix47TXeAIBBBBAAAEEEEAAAQQQCEWgZcuWkpqaaqaP0gVE9cebk7hBgwbVntfXdH993pZ0yy23mLkS9crVQH50Lvxt27bZUj3KiQACCCCAQMQEfL8CVP/AXnjhhbJz505T6G9+85tmlfeqNbjgggvMokh6i/ytt94qN954Y9WXeYwAAggggAACCCCAAAIIBC2gi7HqT9X00ksvyXXXXSdXXnml6GObkwZ3NfBZ9QrQiooKOX78uLnF3wv2enXU/W0K8HrlZosAAggggIDfAr5fAXrvvfdWBj91NcKzzz67xjL36dNHnnvuOZk6darMmTOnxn14EoFwBHRgWNMcUOHkybEIREtApwrRDzSJkrS+jz/+uFxxxRWmyro43Lhx48yCeoli4Eo99UN4WVmZK9WhHgkkwLghgRrb4qo+/PDDZjqx0tJS8X7mz59vaqQXoXjPedv8/HxWh7e4vROh6IwbEqGV3a0j8Qa72tbXAKj+of3jH/8o2dnZ8uyzz4reCp+RkXFGEb0SVFcsnDx5smz99wJJJAT8FND5nHRAuGfPHj+zJS8EIi5QVFQk8+bNk9zc3IifKx5OoO//AwYMkB/84AeVddbB8BtvvCEXXXSRXH/99TVOpxIPZacMpwssWrRIFi5cmFAB/NMVeMZGgby8PDNuKCwstLH4lDmBBUpKSkztDx8+nMAKVN1WgcWLF8uCBQsY69nagAlcbo13abyhoKAggRXsqrqvAdAtW7ZIixYtZPXq1ebKzkAmEz/33HNFP+xzFahdHceG0nrfxnhbG8pMGRFQAa/PeluXVfTKz29/+9uydu3aM1bzhRdekJkzZ57xdV6ILwHtt3oFaE1zgMdXSSkNAtUFvPdcb1v9VX5DIH4FvKvued+N3zaiZGcW0AC+LuylX36TELBJwPvyiXGDPa3mawBUVxk877zzzLw0gRLobRmaPvnkk0APYT8EEEAAAUcE9G6B2oKfXjUfe+wx+fLLL71f2SKAAAIIIIAAAggggAACCCAQsICvAdDu3bsHdfmvzm+3ZMkSU9gjR44EXGh2RCAQgfr1/9O9mfg9EC32iScBr+9623gqm99l+fOf/xxQlnpVy9/+9reA9mWn2Ap4/dbbxrY0nB2BwAW8PuttAz+SPRGIrYDXZ+vVqxfbgnB2BEIQ8Pqvtw0hCw5BICYCXp/1tjEpBCcNSsDXVeD1j26HDh1EP9BOnDixzoLccsstsnfvXrPf+eefX+f+7IBAMAI9evSQZs2aSXp6ejCHsS8CMRfQBeR0oTjdup50saNAUzD7Bpon+/kvMHDgQHMbG18++W9LjpEVyMnJkbS0tFrnr49sCcgdgdAEmjZtag7U/ktCwDYBnQde1xJJSkqyreiUN8EFdO2blJQUFpqzqB/4GgDVej/wwAMyevRo0dvhL7vsshop9u/fL/fdd58888wz5nX9kKRzwJEQ8FMgNTVVunXr5meW5IVAVAT0y6SuXbtG5VyxPklycnLARQhm34AzZUffBdq2bet7nmSIQDQE9EOM3s1EQsA2Ae/KT/38RULANoFE+MLftjahvIEJNG7cmHFDYFRxs5fvfyWHDBki9957r4wfP170KpAJEyaYD/KtW7c287fpqsYvvviiWfjIU7j77rvNvt7vbBFAAAEEEkNA/068+eabAVVW9yUhgAACCCCAAAIIIIAAAgggEKyA7wFQLcD06dPN6q/333+/zJgxo9YyXX/99SZgWutOvIgAAggg4KTA1KlTAwqA6lWFZ7qrwEkYKoUAAggggAACCCCAAAIIIOCbgK+LIFUt1Z133inr16+Xq6++2swLWvU1vcVIrxT9xz/+IbNmzTK3y1d9nccIIIAAAokhcNFFF8l1111Xa2X11r7/+Z//kSZNmtS6Hy8igAACCCCAAAIIIIAAAgggUJPA/7F3H/BVVOnDxx86BAgdAoTeO4IUQRAFKTZQsCHLovwVxAqoqOiigmJhXVEsuIq4YC8orhUEEVApAkuRXgVDbyGhw+tzdk/ekNwk997MTWbm/s7nc5nkzpmZc74z3Mx95pSItAC1B4qPj5cpU6aYXw8cOCBr166VChUqSLVq1YSZsqwSSwQQQCC6Bd58800z4dMLL7xgeg+k1tCWnxr87NmzZ+q3+RkBBBBAAAEEEEAAAQQQQACBoAUi1gI0bQlKlSplWn3qxB5pg58nT56UmTNnpt2E3xHIlkBycrJs3LgxXUAlWztlYwRyQODs2bNmzOTExMQcOFruH0Inwhs3bpysW7dOhg8fbgqkfzN0vOhNmzYR/Mz9UxRSCXbv3i1//PFHSNuQGQE3CBw9elQ2bNggp06dckNxKAMCQQvofYMmrt2gycjoIoE9e/bIjh07XFQiioJAcALcNwTn5KZcORYAzazS3333ncybNy+zLKxDIGQBDaasXLlSEhISQt6WDRDITYG9e/fKihUr5LfffsvNYuT4sWvWrCkDBw40x42Li5P+/fvT7T3Hz0L2D/jrr7/KokWLePiUfUr2kMMC69evl1WrVhHAz2F3Dpd9gcOHD5udJCUlZX9n7AGBHBZYsmSJLF68WLRRFAkBLwloYyu9b9i+fbuXih3VZY1IF/gzZ87Izz//LKtXrzbd3jUynlHSPBr81JngSQg4KaDXoSa7dHLf7AuBSArYa9YuI3ks9o2A0wL2utWltu4lIeAVgdTXrlfKTDlDEzh9+nRoG3gkt20BapceKTbFRMAI8NnLheBVAa5d7505xwOg2oS9b9++dGn33rVAiRFAAAEEEEAAAQQQ8J3A5s2bTZ204QUJAQQQQAABBKJTwPEu8DrrO+N5RufF5LZax8TEmCLZpdvKR3kQyEigSJEiZhXXbkZCvO9mAb1uCxYsSOtPN58kyhZQwH7m2mXATLzpSYGpU6eacuvwMrt27fJkHTIrdKFChcxqWt1npsQ6twroZ26BAgUkf37H22a5tcqUyycC9n7BLn1SLV9Xw9FPmYMHD8qsWbMM2PXXXy+9evWSNm3aSPHixQMi6kDdOlHCI488EnA9byKQHYF69epJ9erVpXDhwtnZDdsikOMCsbGx0r17dxNEyvGDc0AEsinQoUMH0W6YaSc8zOZu2RyBiAvUqVNHqlatyn1DxKVz9gDvvvuumdxKj6rfPXTYrUmTJuVsISJ8NPvgtFixYhE+ErtHwHmB9u3bm/sGAvjO27LHyArUrl1b4uPjuW+ILLOje3c0AHrixAkz3mLr1q3l/fffD6qgOtHF2LFjZdq0aUHlJxMCwQrkyZOHD6NgscjnOgHbmsN1BaNACGQhQAuOLIBY7VoB7htce2rCLlhycrKMGDHinO0nT54sd9xxh7Rs2fKc9/3wi17DJAS8JsB9g9fOGOVNLUBjq9Qa7v/Z0QBo+fLlRYOfNWrUCKnmzZo1C3mbkA5AZgQQQAABBBBAAAEEEIgqgWeeeSbd7LzaQv2ee+4xk7BGFQaVRQABBBBAIMoFHB8DVLu+z5gxw3QxCcV2w4YNoWQnLwIIIIAAAggggAACCCAQUGDbtm3y3HPPBVw3f/58ee+99wKu400EEEAAAQQQ8KeA4wHQu+66Sxo3bhzS2DorV66U6dOn+1OYWiGAAAIIIIAAAggggECOCtx///1y9OjRDI+pXeO1izwJAQQQQAABBKJDwNEu8DoG6Pr162X06NFyww03mFag2i0+s7R161aZMmWKmTAps3ysQwABBBBAAAEEEEAAAQSyEpg7d658+OGHmWb7/fffRbvIP/7445nmYyUCCCCAAAII+EPA0QCoPmVt0aKFaCBUkw4wHmzSGeNJCDgpkJCQYALyOsh90aJFndw1+0IgogLHjx+XRYsWmdmIdUZiEgJeElixYoXoNXz++ed7qdiUFQHZtWuXrF271tzLMpu2dy+IM2fOmDE+g6mBdpEfOHCg+XsbTH635jl58qQpWmYtXt1adsqFwKpVqyQpKUlatWolTOTF9eAlgd27d8uaNWukefPmEhsb66WiR21ZHe0CX6JECbn00kujFpOKu0tAA6AHDhyQffv2uatglAaBLAQOHjxortsdO3ZkkZPVCLhPQMfd02vXfiF3XwkpEQKBBex9w969ewNn4F1PCEyaNEmWLl0aVFk1YPjAAw8EldfNmTR4pEkfPpEQ8JqA3jfo569tROW18lPe6BXYuXOniTdw3+Cda8DRFqBa7d69e8uXX34pffr0MTPCFylSJEMNnYVRL5apU6dmmIcVCCCAAAIIIIAAAggggEBWAocPH5aRI0dmle2c9R988IHceeedcuGFF57zPr8ggAACCCCAgL8EHA+A9uzZU2rWrCnvvvuuFChQICitDh06yLx584LKSyYEEEAAAQQQQAABBBBAIK3AE088IdolMdR0zz33mKFn8uZ1tHNcqMUgPwIIIICAhwQYssFDJ+t/RXU8AFq6dGmZMGFC0MFPLccll1wiJUuW9J4eJXa1QKVKlcx4MmXKlHF1OSkcAmkF9POwbNmyUrly5bSr+B0B1wtUq1bNdMMM9iGo6ytEAaNGQO8bEhMTzedv1FTaRxXViVhfeumlsGq0ZMkSeeutt8x4oGHtIJc3smPdFy5cOJdLwuERCF1A7xt0GIeCBQuGvjFbIJCLAnFxcaJDl5UrVy4XS8GhQxFwPACqB+/Ro0emZdDJPerVq5cyUKw+bWWyhEzJWBmGgH4g6YuEgNcEChUqJO3bt/dasSlvlAnMnj1brrnmGjl27Ng5NdcxvHSIG/0ik/rJuAZEJ06cKDfeeOM5+fkFAbcIlC9fXvRF8qbAsGHDsjWG4MMPPyzXXnttyvcTNyroRF3bt29PV7TNmzeb93Ts5V9//TXd+nz58plJOtKt4A0EXCDQsGFDF5SCIiAQuoAGPgl+hu6Wm1tEJACqFTp06JC89tprsnz5ctOK6dlnn02ppw44ft5558ntt98u9913X8r7/IAAAggggAAC3hDQwOeRI0fk1KlTAQucdjIO/T05OTlgXt5EAAEEsiPw7bffyr///e/s7MJ0nR89erTozPBuTf/6178ynbRp8eLFARuV6OzE+t2MhAACCCCAQDQLRCQA+v3338uAAQNSnlDquKCpU8eOHeXrr7+Wyy+/3MzSqLM1aosnEgIIIIAAAgh4Q0B7e+gDzbQBUG1Bp92ItaWSfum2SVsg0S3earBEAAEnBV5++WWpVatWhrvUzyQdG7RYsWJSoUKFDPPpRK7Dhw+nB1GGQqxAAAEEEEDAuwKOB0C3bdsmvXr1Mq1CMmOpW7euzJkzR2rXrm1uRJ5//vnMsrMOAQQQQAABBFwmkD9/ftFX6mS7vetYdIxHl1qGnxFwn8DWrVtl3Lhx8t5775nCae+tAwcOmCCg3qN7JU2fPj3TomrLyb/+9a9y9dVXi/5MQgABBBBAAIHoE3B8qkMdf0e7xMXExEjXrl3luuuuy1BVB5u/6aabZPz48bJw4cIM87ECAQQQQAABBBBAAAEEnBP49NNPpUGDBmby0n379pkda/BTg6CNGjWSd955x7mDsScEEEAAAQQQQCCXBRwPgM6cOVNatGhhur/reDx9+/bNtIqNGzeWM2fOyLvvvptpPlYiEKqATsKh3TNJCHhRQMdX1M9GEgJeE9DPXhICXhSIpvsGbXhwww03ZHifpJOZ9e/f3/TW8uK5pMwIIOAdAR1KRz9zSAh4UYB4g7fOmqMB0D/++MMMsD1hwgQpVapUUBL6pFmTTpZEQsBJgTVr1sh3330ne/bscXK37AuBiAscPnxY9AESn4sRp+YAERA4ffq02SsB/AjgssuICqxbt87cN+j4tX5POgmpzhieWdL/w0OHDs0sC+sQQACBbAvMnz9ftBGVvX/I9g7ZAQI5JLBhwwZz35CQkJBDR+Qw2RVwNACqEx8UKVJEmjZtGnS5fvrpJ5P34MGDQW9DRgSCEbBPY+wymG3Ig4AbBOw1a5duKBNlQCBYAdsClC8ywYqRzy0C9jPXLt1SLqfLoV/U5s6dG9Ruly5dKvoFj4QAAghESiA5Odk8kEk7qWKkjsd+EXBKQK9dTX6/b3DKyw37cTQAqhMhnHfeebJy5cqg6vbZZ5/JjBkzTN7zzz8/qG3IhAACCCCAAAIIIIAAAuEJhBrQXL9+fXgHYisEEEAAAQQQQMBFAo4GQLVeffr0kccee0x0/LrM0ueffy4DBgxIydK2bduUn/kBAScE8ub97+WdL18+J3bHPhDIMQF77dpljh2YAyHgoADXr4OY7CpHBOw1a5c5ctBcOEjhwoVDOmqo+UPaOZkRQCDqBexnrl1GPQgAnhGw16xdeqbgUVzQ/E7X/d577zXdajp27CiDBw+WPHnymEMkJibK2rVrZfXq1WZ2Sdv1XVd26dLFDLTudFnYX3QL1K1bV2JjYyUuLi66Iai95wTKli1rhhLRJQkBrwnYh0526bXyU97oFahTp44ULVpUKleu7GsEneFdg5pZNVZQBP1/3Lx5c197UDn3C+zfv98UUifKSUpKMv9P3V9qShisQMuWLeX48eNSoECBYDchHwKuEKhdu7YZAjI+Pt4V5aEQWQs4HgDVgOfbb78t1157rQwcONCUQLvGayAqUNIZ4z/++GPRPCQEnBSIiYmRmjVrOrlL9oVAjgjo52iNGjVy5FgcBAGnBXgK7rQo+8spAR3HvlatWjl1uFw7jt4f6Qzvr7/+epZl0Jnig53YNMudkQGBEAQ00Pniiy/K1KlT5bfffjNbbt26VUqWLCkdOnSQO+64Q3r37h3CHsnqVgEe+Lv1zFCurAT0YWI03Ddk5eCl9Y53gdfKFy9eXL755hv5+uuvpVmzZhJoQON69eqZP2iLFi2SEiVKeMmMsiKAAAIIIIAAAggg4FmBsWPHZvmlTVvCjhs3zrN1pODeFViwYIHod8WHH37YBD+LFStmKqMtBPUh2+zZs82wa9qLcN++fd6tKCVHAAEEEMhRgYgEQG0NunfvLsuWLZPDhw/LwoUL5cMPPzTd43ft2iVr1qyRm266yfwRs/lZIoAAAggggAACCCCAQGQFSpcuLT/++KN06tQp4IEuuOACmT9/PsMIBdThzUgK6HWn1+WOHTukTZs28t1334k2mNFUvXp12bNnjxlOTYe4+v777+XCCy+UgwcPRrJI7BsBBBBAwCcCEQ2AWiNtEdqqVSvTLV7/SJUvX96uMn/U1q1bl/I7PyCAAAIIIIAAAggggEBkBSpVqmRa0s2ZMyclEKqBTw0q6Vj91apVi2wB2DsCaQQOHDgg11xzjRmf9tZbbzVB+EsvvdSMRWuz6rBqgwYNkiVLlpiehtqo5uabb7arWSKAAAIIIJChQK4PvKljusybN0+eeOKJDAvJCgQQQAABBBBAAAEEEHBeQCcu1SDTDz/8YAKhl1xyifMHYY+OCOjYrYHOz+LFi83ks+eff75pHZn2YF6ZlE6HZti9e7dcfPHFph6ZjSldsWJF+fe//y2NGzeWzz77zLRo1muZhAACCCCAQEYCIQdA169fL2+++aaZpU3HYdE/qPrHyf6B0sk79KUp7c+p3zt79qxs27bNdIvXGTcJgBoy/nFQIDk5WRISEkx3Ga/c+DlYfXblYQH9fNy8ebOUK1fOjKns4apQ9CgUOHPmTBTWmir7QeDo0aOm2612s2VyTj+cUf/VoUKFCqKvtOnQoUPmLZ3IS2fU9mLSvx2TJ082RdexZ+13y8zqojMvDxs2TEaNGiWTJk0SAqCZabl3nQ5rcOLECdFxh0kIeEmA+wYvna3/ljXkAKh2l9EZ+fRkO5m0q027du2c3CX7inIBHVpBZ4ssVKiQ6A0SCQGvCOzdu1dWrFhhxl7T8a9ICHhJ4PTp06a4dumlslPW6BbQh/z68KlgwYJStWrV6Mag9p4S0PkWNOnM6V5Ny5cvN+N71q5dW1q0aBF0Na6//noTAJ05c2bQ25DRXQI6nMGxY8fMMHnawIqEgFcENm7cKPrSh6b68JTkfoGQxwDV1praTcbpZJ/4Ob1f9he9ArYVkl1GrwQ195qAvWbt0mvlp7wIqADXL9eB1wTsNWuXXis/5Y1eAe05oskuvSixfft2U+z69euHVPw6deqYHona64sHbyHRuSaz/cy1S9cUjIIgkIWAvWbtMovsrHaBQMgtQLXMPXr0kE2bNpmxWdI2VddWS9ddd53cf//9ZvwWHUA9oy4Ms2bNkieffFJeffVVM4i1CzwoAgIIIIAAAggggAACCCCAQA4K2CHUQg3i2vx2+xwsModCAAEEEPCYQFgB0C5dusiWLVukffv26ap74403yvvvvy89e/ZMty7tG7fccosZJ3Tw4MGybNmytKv5HYFsCcTExJjt7TJbO2NjBHJQQMfw0sS1m4PoHMoxAfsllLGXHSNlRzkkYD9z7TKHDsthEMi2gA73pMnLn7tVqlQxdVi9erVZBvuPDnmlLT91ey/XP9j6+jGffuZqIJuxl/14dv1dJ3u/YJf+rq0/ahdWAFTHZhkwYEA6AR1/5tSpU0EFP+3Gffr0kdtvv1103JZrrrnGvs0SgWwL1KtXz4zFUbhw4Wzvix0gkJMCsbGx0r17dzMOXU4el2Mh4ISA/QKaUe8PJ47BPhCIhIB2pdWxP7lviIQu+4ykgH1wWqxYsUgeJqL7btKkiRn7XHsZLly4UFq3bh3U8d577z2TLxJDtAVVADJlW0AbVWkA1N4/ZHuH7ACBHBLQuJjONcJ9Qw6BO3CYsAKgetxA47PoEzidJCmUpOMlnDx5kgBoKGjkDUpAWyHxYRQUFZlcKGBbc7iwaBQJgUwFbAvQTDOxEgEXCnDf4MKTEkaRdEKVuXPnnrOlBtQ0aevC8ePHn7NOJ64IpufaORu59Bcvf/5q2bV34FNPPSXDhw+X2bNnZ9kiUCcte+GFF8zZGDhwoEvPCsXKSoCWn1kJsd7NAsQb3Hx20pct7ABo+l2JNGrUSH799ddAqzJ8T29QNAiqM2+SEEAAAQQQQAABBBBAAIFwBfr162cCnYG2X7x4segrbdqxY0fIjTjS7oPfsy/wwAMPiE6MO2/ePBMMffPNNyWjWcG3bdsml19+uSQmJpr5J9q1a5f9ArAHBBBAAAFfCzgaANXuFzopkk5sNHLkyCzhNm7cKEOGDDH5WrRokWV+MiCAAAIIIIAAAggggAACGQmMGzdOvvvuu3NWnzhxQpYuXSp169aVUqVKnbNOW4CG2oPtnB3wi2MCJUqUkGnTppmJdKdMmSLLly+Xxx9/XLSbqU179+4VXaffN/ft2ydNmzaVN954w65miQACCCCAQIYCjgZA9SiPPPKI9OrVS7Zv3y733Xef1KpVK93B9Und1KlTZfTo0ZKQkGDWd+zYMV0+3kAAAQQQQAABBBBAAAEEghW47LLLRF8kbwro2J/z5883c0P85z//Md8rCxYsaCqjXd4rVKhgeg/qG1dccYX5Tlm8eHFvVpZSI4AAAgjkqIDjAdArr7xSxo4dKyNGjJCJEyeaAaxr1qxpnqzu379ftm7dKosWLTLdFWxNdUIl7cJAQgABBBBAAAEEEEAAAQQQiF6B5s2bm2EMXnvtNdPa0w6xppPt6hjpXbp0Mb0ICXRH7zVCzRFAAIFwBBwPgGohdPwW/eOkXRYWLFhgXhkVTgOm+seNhIDTAtq6WMeWbdmypRQtWtTp3bM/BCImcPz4cfOgSGcj1hcJAS8JnD592kvFpawIpAjs2rVL1q5dKzosk5dn006pED9EjYBOKKvp6NGjvqmzfpe85557zEtbgmpQVIcr0El3MxoX1DeVj7KKrFq1SpKSkqRVq1bi5Ym8ouy0Ud0/BXbv3i1r1qwxn0+xsbGYeEAgb6TKqH+wdIxPncWvXr1658zip90Y2rRpI998841Mnz7dBEsjVQ72G70CGgA9cOCAGR8oehWouRcFDh48aK5bnZSBhIDXBHRiQ032C7nXyk95o1fA3jfoGIMkBLwkoMEjTfoA1Y8pJibGVEsDnwQ//XeGdUIr/fzVsXpJCHhJYOfOnSbewH2Dd85aRFqA2urrIOM6ELm+9IuQBkTz5s1rxgXNly+fzcYSAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIiEBEA6CpS6xP6+rXr5/6rXN+1qc+FStWPOc9fkEAAQQQQAABBBBAAAEEEEAAAQQQQMBNAgzZ4KazEVxZItYFPrjD/zfXoUOH5PXXXw9lE/IikKVApUqVpHTp0lKmTJks85IBATcJlCxZUsqWLSuVK1d2U7EoCwJBCWhPD010UwyKi0wuErD3Dfr5S0LASwJ2rPvChQt7qdiUFQEjUK1aNTNhsg6TR0LASwJxcXEm3lCuXDkvFTuqy5pjLUAzUj579qwJfuqShICTAvqBpC8SAl4T0IH/27dv77ViU14EjABD3HAheFWgfPnyoi8SAl4TsA+cCIB67cxRXhVo2LAhEAh4UkADnwQ/vXXqwgqA/u1vf5MffvhBHn30Ubn00ktTaqwTd+iX9mBngNVxQXXA2MOHD8uoUaNS9sMPCCCAAAIIIIAAAggggAACCCCAAAIIIICAEwIhB0CXL18uo0ePNsfWGd71d5u026Z2H5o5c6Z9iyUCCCCAAAIIIIAAAggggAACCCCAAAIIIJBrAiEHQGvUqCElSpQQHbezefPm6Qrev39/EwAtUqSICYZm1hVDW4Du2bNHDhw4kG4/vIEAAggggAACCCCAAAIIIIAAAggggAACCGRXIOQAaPHixWXTpk2ybNkyueiii9Idv2fPnmb8pCVLlgQ9gcdzzz0nSUlJ6fbFGwgggAACCCCAAAIIIIAAAggggAACCCCAQHYEwpoFXmfW1tmJA01cFBsbKwMGDAg6+KmFv/nmm7NTB7ZFIKCAXp9Hjx4NuI43EXC7wLFjx+TMmTNuLyblQyCdQKB7g3SZeAMBFwpw3+DCk0KRQhLg8zckLjK7RODUqVNy4sQJl5SGYiAQmgDxhtC8cjt3WAHQhx9+WOrXr3/OBEipK3LXXXel/jXLn8uWLSt9+vTJMh8ZEAhFYM2aNfLdd9+ZYRZC2Y68COS2gE4M9+23354zxnJul4njIxCsgJ0IkQB+sGLkc4vAunXrzH3Drl273FIkyoFAUALJyckm35EjR4LKTyYE3CQwf/58M4SevX9wU9koCwKZCWzYsMHcNyQkJGSWjXUuEgg5AKpPFl955RVThf379wesSnx8fMD3M3uzcePGma1mHQIhC9inMXYZ8g7YAIFcErDXrF3mUjE4LAJhCdgWSHyRCYuPjXJRwH7m2mUuFoVDIxCSgG09x+duSGxkdomABvB1bhBtCUpCwEsC9uET9w3eOWshB0BXr15tJkDq0KGDfPPNN+lq2rp1a9m6dWu693kDAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIaYGQA6AbN26U8847T77//nupWLFiuvL+/vvv5glOuhWZvKFPe5599tlMcrAKgdAF8ub97+WdL1++0DdmCwRyUcBeu3aZi0Xh0AiELcD1GzYdG+aSgL1m7TKXisFhEQhZwF6zefLkCXlbNkAgtwXs9WuXuV0ejo9AsAL2mrXLYLcjX+4JhDwLfNWqVaVGjRpSoEABx0qtYyfY5sOO7ZQdRb1A3bp1RSfliouLi3oLALwloOMiN23aVHRJQsBrAvahk116rfyUN3oF6tSpI0WLFg1pIs/o1aLmbhIoXry4KY5evyQEvCbQsmVLOX78uKPxBa8ZUF5vCtSuXVuKFCki4QwB6c0ae7/UIQdA9Uv5li1b5K233pIrr7wyW1/QteXnH3/8IY899piZVMn7nKHVQIO+X331lWgAePv27bJjxw7RyU/KlCljXKtXry6dOnUS/aPAF8nQbDV3TEyM1KxZM/QN2QKBXBbQFhz6oImEgBcFeAruxbNGmVVAv8TUqlULDARcLTBixAgZP3682PGWtbB20rl58+ZJoUKFzim/PkxdsGABX9DPUeEXNwnwwN9NZ4OyhCJQuHBh7htCAXNB3pADoPrF/Pnnn5cePXrILbfcYm4WU3/Z0aCezhAfTNI/1vaP96hRo4LZxBd5Zs2aJZMnT5Zp06ZJMLM1aivGPn36iN7waKtGEgIIIIAAAggggAACCESfgH7X0tZygZJ+r7ITItn1mp/JkawGSwQQQACBaBYIeQxQxbroootkzpw50q5dO9EZr5KSklJe+odX/8gG87LBz2g5Abt375bevXtL586dZcqUKUEFP9VGW4VOmjRJGjRoIEOGDGGGvGi5YKgnAggggAACCCCAAAKpts1wDQAAQABJREFUBF566SUT5NQgaDCvffv2SbVq1VLtgR8RQAABBBCIToGQW4BaplatWsn8+fNl165dojPD//bbb2b5xhtvmG7bwYxBo3+0dcb4FStW2N36drl3717p2LGjrF271tRRx1Jt0qSJ6HhTVapUMd21tcu2NqPWoHJiYqJ5afBTt1m4cKHpIv/qq6+KTkT1xRdfSMGCBX3rRcUQQAABBBBAAAEEEEAgvYCTczGk3zvvIIAAAggg4E+BsAOglqNChQqiLx2rUtPHH38s+mRSB4QNNk2YMEE0QOjnNHDgQDPW55133im33nqrmeAk1PrqeKlPP/20vPzyy6Jmw4YNC3UX5EcAAQQQQAABBBBAAAEEEEAAAQQQQCCqBMLqAu+00F/+8hfRsUX9mrSF7LfffiuzZ882wWGdSCqcVKlSJXnxxRfl66+/ljFjxqQMeB7OvqJhGx3zSFvLMu5RNJxtf9VRhwfZtGmTaQXur5pRm2gQsJNxRENdqaO/BLQHjk5MqZN0khDwkoDeN2zevNkMm+WlclNWBFRgz549pqcjGgh4TYD7Bq+dMRHHA6CvvfaaxMXFhSRRokQJMzZmSBt5KLPOvDho0CDp0KGDI6Xu2rWrmSVeb3RIGQusW7dOVq5cKQkJCRlnYg0CLhTQFvE6NIgOLUJCwGsC9qGTXXqt/JQ3egXWr18vq1atEu1xQ0LASwL79++X5cuXm+vXS+WmrAiowJIlS2Tx4sVy8uRJQBDwlIA2ttL7hu3bt3uq3NFcWMcDoD179pRixYqFZKofdjt37gxpGy9l1smPGjVq5GiR27Rpw3+0LERtKyS7zCI7qxFwjYC9Zu3SNQWjIAiEIMD1GwIWWV0hYK9Zu3RFoSgEAkEI2GvWLoPYhCwIuEbAXrd26ZqCURAEshCw16xdZpGd1S4QyPYYoE7U4bvvvpNFixZJly5dnNid6/YRHx8vc+bMcaxc2s1l2bJl0qxZM8f2yY4QQAABBBBAAAEEEEAAAQQiJ6BDot1///3ntHa0wZNjx46l+35XqFAh0UlwW7ZsGblCsWcEEEAgSgQiEgDVD/Gff/7ZzAqvM5jr2AgZJR0fc968efLQQw9llMXz72trzQEDBkiPHj2kV69e2a7P448/Ljr7Y8mSJbO9Lz/vICYmxlTPLv1cV+rmL4EiRYpw7frrlEZVbeyY3vny5YuqelNZ7wvY+wW79H6NqEG0COh9g372cu26/4wvXbpUfv3114AF1UYuOpRB2qRdbP0cANXrVuueP39EQhNpOfkdAccE7GeuXTq2Y3YUMQHHP2V0EOO+ffvKzJkzI1Zor+24Vq1actVVV8nVV18t1157rRkP9KKLLgrpQ17HUps1a5aMGzdOtMWsPgkkZS5Qr149qV69uhQuXDjzjKxFwGUCsbGx0r17dylYsKDLSkZxEMhawAY+8+Z1fJSdrA9ODgSyIVCnTh2pWrUq9w3ZMGTT3BHQ4ce6detmGkjkTgk4arACQ4cONY1i0o53efjwYdG/m2mHktPvMXXr1g12957M1759exMAtfcPnqwEhY5Kgdq1a4v29iXe4J3T73gAtF+/fgQ/A5x/nRxKJ+T56KOPzEsnftJxQfU/jQbpihYtap7a6hNcnX00KSlJdBbzHTt2mJa0OiGKDnCuaeTIkTJ48OAAR+Gt1AL6JJwPo9Qi/OwlAe3yRELAiwK2BagXy06Zo1uA+4boPv9erz33Dd44g/o506BBA28UNodKScvPHILmMBERIN4QEdaI7dTRAOjBgwdNK0Ut7fXXX2+6e2v37+LFiwesgAb6dKbNRx55JOB6P71ZtmxZ0TFfdDb4L774Qg4dOiQ//fSTeQVbTw2ODhs2TMaMGRPsJuRDAAEEEEAAAQQQQAABBBBAAAEEEEAgqgUcDYCeOHFCdPzP1q1by/vvvx8UbFxcnIwdO1amTZsWVH4vZ6pYsaJMnz5dvvnmG5kyZYoJhCYmJmZZpXLlyskdd9whQ4YMEf2ZhAACCCCAAAIIIIAAAggggAACCCCAAALBCTgaAC1fvrwJftaoUSO4o/8vl85mHuo2IR3AZZl1bD996Ux/Olbq+vXrJSEhQXbu3Ck6/osGSnUMKn1VqVJFWrVqJXZSFJdVheIggAACCCCAAAIIIIAAAggggAACCCDgagFHA6BaU+36/uSTT5pxLEMZz2PDhg3SokULV2M5XTgdL+KKK65werfsDwEEEEAAAQQQQAABBBBAAAEEEEAAAQT+J+D4FK133XWXNG7cWCZNmhQ0sk4OpF3DSVkL/OMf/zCTJjEJUtZW2qr2xx9/NBNKZZ2bHAi4R+D48eMyb9482bZtm3sKRUkQCFLg9OnTQeYkGwLuEti1a5e5bzhy5Ii7CkZpEMhCQIch0/uGLVu2ZJGT1Qi4T2DVqlWycOFCMxO8+0pHiRDIWGD37t3mvkF78ZK8IeBoC1D946vduUePHi033HCDaQWq3eIzS1u3bjXjYfbq1SuzbKz7U0AnmXrwwQdFnSdOnCj9+vWTCy+8MOI2X375pUyePNmRP0qbNm0y5Z0zZ45ccskloq2EY2NjA9YhKSlJNBBkU0xMTMBZ3c+ePWuGDrBfunV2Rd2nfhAdPXpUSpYsKbqtppMnT4p+sdFtNEXy+Pny5TPHSP0Px8c/mOtPh8fYv3+/5M2bVwoWLGguoVCvf69ef/YGQoO/+hlhk1/qr5/jmpKTk6Vr164R+/zLzfOv16+mm266SVLPSsznH59/wXz+6bWT3b//4V7/+v9T/2/qsEP2PkHL45fPn8z816xZo1WVTz/9VJYuXZqt+69w/U0B/vwnt86/l4+vczDo9wO9Z9CJV9Mmr3/+avk1aeOGPn36mJ8zuv/n+vPe9w8dBk6v4QoVKpgh4rLz/Y/z773zb/5D/+8fr33+6z2vxiAKFCggpUuXlkhef/qQgJR9gTx//vH4byQo+/syM5trwFP/AIeaRo0aJY899liom0VVfv3DULt2bdm8ebP5T6bBYx0vNNLp6quvls8++yzSh2H/CCCAAAIIIIAAAggggAACCCCAAAIBBHQybW0IRwpPwNEWoCVKlJBLL71UtMUgyXkBbQ2mLSc/+OAD6dKlS44EP7UWr7/+uvTv3988mcture6//34TwNWJr3Rme20dVLRo0YC71ad/+hTIpuLFi5vAr/099TIxMdG07tT31EmvRQ0U792710ywVa5cOZNdn9BoCzMb94/k8bUlatrE8fEP5vrTVkjr1q0zrY4qVapkLqNQr3+vXn/bt2+Xe++9V7TeqR+K+aX+2lVGh4rRzx79bI3U519unv8hQ4aYB6E6FE7qFv58/vH5F8znn37gZffvf7jXv9437NmzR+Lj48/pceKXz5/M/D/55BN57733RB96awu77Nx/hetv75ly6/x7+fja42nHjh3mM7d+/fq2KilLr3/+6v28/u2Mi4uTCRMmpNQr0P0/15/3vn8sWbLE9Bw977zzzPdNPn/C//7L9Z+z1/8ff/yR0giwWrVqEkn/F1980XS3T/kA5IewBBwNgGoJevfubQKgevPUunXrTGcv1yCU/kGbOnVqWIWPxo10Vvj77rsvR6uuwUO9IXYi6QRZmmrUqCG33nqrE7vMcB/anUKHZNDJtTIKMmS4MSsQyEUB/fK3ePFi0f/vVatWzcWS5PyhV69ebQ6qDzEi/RmR87UT0S9r+iVOuynqgyU/pnvuuccEQPV+IHUA1I91pU7+EtAHFGvXrhX9El6sWDF/VS6L2mi9NWnwrG/fvlnkZrXbBLT33aJFi0zwXr+E+y3p/bwmfRihf1tI/hJo0KCBeehy/vnnBwwg+au21MZPAvrQVIeQad68ufl8imTd6JHrjK7jAdCePXtKzZo15d13382wtV7aonfo0MEM3J32fX5HIDsC+pRYXyQEvCagrQPbt2/vtWJTXgSMQKDxj6BBwAsCOoxTVmPXe6EelDH6BPShGvcN0Xfe/VLjhg0b+qUq1CPKBLShmO1pGmVV92x1HQ+A6uCv2jVBB4INNulEFzpRDQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHBSIK+TO7P76tGjh/3xnKUdd/GcN//8Rcds1CbvJAQQQAABBBBAAAEEEEAAAQQQQAABBBBAwEkBx1uApi6cDgr797//XZYvXy6rVq2Sffv2Sd26daVx48aiXeVvuOGG1Nmj7ufk5GT56quvZMOGDaITf+jg5TpAfZkyZaRs2bJSvXp16dSpk7Rs2VLoUhh1lwcVRgABBBBAAAEEEEAAAQQQQAABBBBwQCAiAVBt6fnPf/5THnjgATMrVupyrly5UvT1/vvvyyuvvCIvvfSS6Izg0ZRmzZolkydPlmnTpsmRI0eyrLpOIqGTSo0YMcIEkLPcgAwIIIAAAggggAACCCCAAAIIIIAAAgggYAQi0gVeW30OGjQoXfAzJibGTEpjWzPOnTvXDNhtZ/31+znR2UV15sLOnTvLlClTggp+qom2Cp00aZLoDHlDhgyRU6dO+Z3KkfppIP7o0aOO7IudIJDTAseOHZMzZ87k9GE5HgLZFshouJts75gdIBBhAe4bIgzM7iMqwH1DRHnZeQQF9LvtiRMnIngEdo1A5ASIN0TONhJ7drwF6Nq1a+XRRx81ZS1VqpRcf/31cuONN0qjRo1M125dcfr0adPd+5NPPpHXXnvNtG5cuHChFC1aNBJ1dMU+9+7dKx07dhT10VS1alVp0qSJ1KlTR6pUqSIaHNZX4cKFTdAuMTFR9KXBT91GfbSL/KuvviobN26UL774QnTGR1LGAmvWrJF169ZJu3btmJ0tYybWuFBA/9/Pnj1bqlWrJs2bN3dhCSkSAhkL6N94TQTwMzZijTsF9J5B7x3atm0rFSpUcGchKRUCAQT0O4P2MNPvFC1atAiQg7cQcK/A/PnzJSkpSbp168awb+49TZQsgIAOZahDPbZu3VoqVqwYIAdvuU3A8QDo3/72N9EnkP3795eJEyeagF7aSmsLUA0ADh06VG699Va58sor5eWXXzZd5tPm9cvvAwcONGN93nnnnabOTZs2DblqOqbq008/bawmTJggw4YNC3kf0bSBfRpjl9FUd+rqbQF7zdqlt2tD6QMJ+LmVpK2bDYQGqj/vIeBGAfuZa5duLCNlQiCQgH730sS1G0iH99wuoPNinDx50vRytD1F3V5myoeACui1q4nPXsPgiX8c7wK/ePFiM1bl22+/HTD4mValWLFiosE8beHo16Rd/L/99lvTokvHPA0n+Kk2lSpVkhdffFG+/vprGTNmDK1r/HrBUC8EEPC9AF29fH+KqSACCCCAAAIIIIAAAgi4SMDRAKhO6LNlyxa5++67Q6piw4YNzcRIIW3kocwLFiwwY6J26NDBkVJ37drVzBK/efNmR/bn153kzfvfy5sniX49w/6tl7127dK/NY2+mi1fvtxUWgOgv//+u68BuH59fXp9WTl7zdqlLytJpXwpYO91uXZ9eXp9Xyl73dql7ytMBX0jYK9Zu/RNxXxcEUe7wGvTXx3TUlsqhpJ+/fVXM+5HKNt4Ka9OfqRjoDqZ2rRpI9u3b5datWo5uVtf7atu3boSGxtrJt7yVcWojO8FypYta1qK65LkL4EHHnggpUL683vvvZfyu19+sF/E7dIv9aIe/hfQe1gdj75y5cr+ryw19JWAzrvQrFkzKV26tK/qRWWiQ6Bly5Zy/PhxKVCgQHRUmFr6RqB27dpSpEgRiY+P902d/F4RR1uAlitXTjT6rWN4BJt0rDCdCMnPA3brfwgN8jqV1GzZsmXmRsepffpxPzqpVM2aNRlM248n1+d1ypMnj9SoUUOKFy/u85pGV/U++ugj+emnn1Iq/f7774sO/O+3xFNwv53R6KmPfonRB8sE76PnnPulpnrfUL16dfPg3y91oh7RI6AP/HnwFD3n20811Qms9b4hf35H2xX6ich1dXE0AKq104DTE088EVRFtcWozhL/5ptvmpmzgtrIg5m0teZbb70ln332mSOlf/zxx80TspIlSzqyP3aCAAIIIBBZAZ2g4v777093kHvuuYfxnNOp8AYCCCCAAAIIIIAAAggg4KyA4wHQESNGyFNPPSUXX3yxLFmyRALNArtq1SrzRVCDpdoiRp9Y6uzofk36VOCqq66Sq6++Wq677jr5/vvvzSx3odRXHWfMmCHdunUTDYDedtttoWxOXgQQQACBXBR47rnnZOvWrelKoL0DJk+enO593kAAAQQQQAABBBBAAAEEEHBOwPG2ujrRz8MPP2xmKdfxPLQ5cNWqVaVixYqyZ88e2bZtm2hLGJu0m9HUqVOlRIkS9i1fLrWb/8qVK03AV4O+Wl8dF1THjdAAsI45pV22tfvVqVOnzJioycnJsmPHDtFZ5FesWCH79+83NiNHjpTBgwf70olKIYAAAn4T0M/xZ555JsNq6d/Ma6+9liEPMhRiBQIIIIAAAggggAACCCCQPQHHA6BanFGjRpmWn+PGjTPjgW7atEn0lTZp4O/VV1+V9u3bp13lu991bJPZs2eb2eC/+OILOXTokBkLLvV4cFlVWoOjw4YNM8HlrPKyHgEEEEDAHQLaMyIpKSnDwuzatUtGjx4tzz77bIZ5WIEAAggggAACCCCAAAIIIBC+gONd4LUo2upTu8EvXbrUdPuuUqVKSgk1iKcTHmmLF+0K371795R1fv9BW8FOnz5dvv76a+nbt2/QrX10cqnHHnvMdJ8cM2aM35kcq5+2oN24cWPAYRgcOwg7QiACAjrRmT40SkxMjMDe2WVOCvz888/y7rvvZnnI8ePHy4YNG7LM54UMZ86c8UIxKSMC6QR0bHr9f6g9cUgIeElA7xs2b94shw8f9lKxKSsCRkB7iWpvGRICXhPgvsFrZ+zPWGUki6xdvD/99FNzCG3xuG/fPtPdO9pniNWgr750KICZM2fK+vXrJSEhQXbu3GluXDRQqsMG6EuDx61atTJd4yN5rvy473Xr1pmgcaFChSQ+Pt6PVaROPhXYu3evGfYiLi5OdBI1kjcF9AupTnKky6zSiRMnZPjw4fL5559nldX16+3Y33bp+gJTQAT+J6D3YxpEKliwoLkHAwYBrwjoMFnLly+X8uXLywUXXOCVYlNOBIyAzhui34v1+i1QoAAqCHhGQBtb6UsbAGrvZpL7BSIaAE1dfR3zMvU4n9o6T28w9WKJ1lS4cGG54oororX6Ea+3bYVklxE/IAdAwCEBe83apUO7ZTc5LPD222/LokWLgj6q9hDQye4uvfTSoLdxc0auXzefHcoWSMBes3YZKA/vIeBGAXvN2qUby0iZEMhIwF63dplRPt5HwG0C9pq1S7eVj/KkFwg5+qitVO6///6USXp0XDMNZtrll19+KZUrV05/pDTv6Ky3ixcvlkmTJqVZw68IIIAAAgh4W+DIkSNmqJdQazF06FBZtmxZVD8cDNWM/AgggAACCCCAAAIIIIBAVgIhjwGqzdK1C/ubb74p7733nhnT8pdffpGLL75YPvnkk6CCn1qoQYMGme7JjzzySFZlZD0CYQnExMSY7ewyrJ2wEQK5IKBjJWvi2s0FfIcO+eSTT5qhTULdnY6N/dprr4W6mavy58mTx5QnX758rioXhUEgKwH7mWuXWeVnPQJuEdD7Bv3s5dp1yxmhHKEI6HWrMYZo7hkaihd53SNgP3Pt0j0loyQZCYTcAlT/uI4cOVJeeOEFs8+OHTvKO++8E/IYi/rFSAOoOiFS8+bNpU+fPhmVkfcRCEugXr16Uv3PsTh0qAESAl4SiI2NNeME6zAhJO8J6ARW//jHP8Iu+KhRo8xEeaVLlw57H7m5oQ18Rvt437l5Djh2eAJ16tQxY39y3xCeH1vlnkCxYsWkW7dujJ+Ye6eAI2dDoH379ma8dHv/kI1dsSkCOSpQu3ZtEwfjviFH2bN1sJBbgOrR1qxZYw5as2ZNmTZtWsjBT1tiHej4b3/7m4wePdq+xRIBxwQ0WM+HkWOc7CiHBXTyLtuSLocPzeGyKaCTGR0/fjzsvehkFvq30auJ69arZ45yc9/ANeBlAb1v4MGTl89g9JZdW34y+VH0nn+v15x4g7fOYFgB0FmzZpkuFp999plkt4VK3759zYyb33zzjbfkKC0CCCCAAAJpBPTvo/5tzG7SbvDaHZ6EAAIIIIAAAggggAACCCCQfYGQu8DrIfVLmY752aRJk2yXQLtsdOnSxXSj7969e7b3xw4QQAABBBDILQEdh02Hd8koHT16VG655RbTOv2tt97KKJt5/+TJk5muZyUCCCCAAAIIIIAAAggggEBwAmEFQNevXy89e/YM7ghB5KpRo4bMmzcviJxkQQABBBBAwL0CF1xwgegro5SYmGgCoNrV64YbbsgoG+8jgAACCCCAAAIIIIAAAgg4KBBWF3gNgLZs2dKxYsTFxcmGDRsc2x87QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEVCDkAOipU6ckKSlJDh065Jjg2rVr5cCBA3LmzBnH9smOEEhISJAff/zRXK9oIOAlAZ1AR1vFb9u2zUvFpqwIGIHTp08jgYAnBXbt2mXuG44cOeLJ8lPo6BU4ceKEuW/YsmVL9CJQc88K6PB6CxcuNDPBe7YSFDwqBXbv3m3uGw4fPhyV9fdipUMOgOosbaVKlZJffvnFsfouXrzYTKbEzIWOkbKjPwU0AKqB9X379uGBgKcEDh48aK7bHTt2eKrcFBYBFbAPMxnDlOvBawL2vmHv3r1eKzrljXIBbZii97vcN0T5heDR6usDf/381UA+CQEvCezcudPEG7hv8M5ZCzkAqlWLj493LACqf7D1qU/FihW9o0ZJEUAAAQQQQAABBBBAAAEEEEAAAQQQQMATAmEFQDt37izaanPKlCnZruRtt90m2q2+Q4cO2d4XO0AAAQQQQAABBBBAAAEEEEAAAQQQQCCSAnny5Ink7tl3BATCCoBeddVVpigavNTxOsJNr7zyinz44Ydm8x49eoS7G7ZDIKBApUqVzNAKZcqUCbieNxFwq0DJkiWlbNmyUrlyZbcWkXIhkKGAHc5GZ7onIeAlAXvfoJ+/JAS8JFCiRAlz36C99EgIeE2gWrVqop+/BQsW9FrRKW+UC+hk3qVLl5Zy5cpFuYR3qp8/nKJ26tRJLrzwQjPYdq9eveTFF1+U3r17S7ARcJ3gY/z48fK3v/3NHL5BgwZy2WWXhVMUtkEgQwH9QNIXCQGvCRQqVEjat2/vtWJTXgSMQL58+ZBAwJMC5cuXF32REPCagAaOuG/w2lmjvFagYcOG9keWCHhKQAOfBD89dcpCnwXeVu/5558Xbd2hAxZfe+210rx5c5k2bVrK5Ac2X+qldnXXFp8a8BwxYoRoIFTT008/LXxhSi3FzwgggAACCCCAAAIIIIAAAggggAACCCDghEBYLUD1wK1atZJ//vOfMmDAAFOO5cuXyzXXXGOColWrVpUaNWqYV0xMjGzYsEHWrVsnmzdvNuN9mg3+98+DDz4otkt96vf5GQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyK5A2AFQPfBf//pX0+196NChsn//flOWkydPysaNG80rs8Llz59fhg8fLk899VRm2ViHAAIIIIAAAggggAACCCCAAAIIIIAAAgiELRDWJEipj9a/f39Zs2aN/OUvf0n9dqY/6xg1S5YsMV3fgx03NNMdshIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAggkK0WoHZ/OvDrv/71L7n55pvlk08+kZUrV5rXvn37TJYiRYpI/fr1pVGjRtK1a1fp169f0BMm2WOwRCBUgbNnz8qxY8dErz8SAl4T0GtXJzWwM2p7rfyUN3oF9LOXhIAXBbhv8OJZo8xWgPsGK8HSawI6T8iZM2eYBd5rJ47yGoGjR48Sb/DQteBIANTW9+KLLxZ92bRz505JSkoyY4HyJd6qsMwpAW2ZrGPPtmvXjtnZcgqd4zgicPjwYZk9e7ZUq1bNTDDnyE7ZCQI5JHD69GlzJP0yQ0LASwJ6z6D3Dm3btpUKFSp4qeiUNcoFEhMTZdasWVKlShVp0aJFlGtQfa8JzJ8/38QMunXrxsTIXjt5UV5enetm1apV0rp1a6lYsWKUa3ij+o4GQNNWOS4uLu1b/I5Ajgno0xhNdpljB+ZACGRTwF6zdpnN3bE5AjkqYFuA2kBojh6cgyGQDQH7mWuX2dgVmyKQowLa+lMT126OsnMwhwSSk5NF5xHRlqD58uVzaK/sBoHIC+i1q4nP3shbO3WEbI8B6lRB2A8CCCCAAAIIIIAAAggggAACCCCAAAIIIOC0AAFQp0XZn2sE7LALPEl0zSmhIEEK2GvXLoPcjGwIuEqA69dVp4PCBCFgr1m7DGITsiDgCgF7r8u164rTQSFCFLDXrV2GuDnZEcg1AXvN2mWuFYQDBy0Q0S7wQZeCjAhEQKBu3boSGxsrDMUQAVx2GVGBsmXLStOmTUWXJAS8JmC/iNul18pPeaNXoE6dOlK0aFGpXLly9CJQc08KlCpVSpo1ayalS5f2ZPkpdHQLtGzZUo4fPy4FChSIbghq7zmB2rVrmwmQ4uPjPVf2aC0wAdBoPfNRUO+YmBipWbNmFNSUKvpNIE+ePGbyOL/Vi/pEhwBPwaPjPPuxlkWKFJFatWr5sWrUyecCet9QvXp1n9eS6vlVgAf+fj2z/q9X4cKFuW/w2GmmC7zHThjFRQABBBBAAAEEEEAAAQQQQAABBBBAAIHgBQiABm9FTgQQQAABBBBAAAEEEEAAAQQQQAABBBDwmAABUI+dMIqLAAIIIIAAAggggAACCCCAAAIIIIAAAsELEAAN3oqcCCCAAAIIIIAAAggggAACCCCAAAIIIOAxAQKgHjthFDd4geTkZNm4caOcPn06+I3IiYALBM6ePSubNm2SxMREF5SGIiAQmsCZM2dC24DcCLhE4OjRo7JhwwY5deqUS0pEMRAITkDvGzZv3iyHDx8ObgNyIeAigT179siOHTtcVCKKgkBwAtw3BOfkplwRnwX+gw8+kLlz50pCQoLUqVNHmjZtKpdddpmULFnSTQ6UxYcC69atk61bt0qhQoUkPj7ehzWkSn4V2Lt3r6xYsULi4uKkTZs2fq0m9fKpgH3oZJc+rSbV8qHA+vXrTRCpYMGCUrVqVR/WkCr5VWD//v2yfPlyKV++vFxwwQV+rSb18qnAkiVL5NixY+b6LVCggE9rSbX8KKCNrfSVP39+qV69uh+r6Ls6RbQF6JAhQ+S5554zgc+RI0dK69atZfXq1dKuXTt55JFHZN++fb4DpULuEbCtkOzSPSWjJAhkLmCvWbvMPDdrEXCnANevO88LpcpYwF6zdplxTtYg4C4Be83apbtKR2kQyFzAXrd2mXlu1iLgHgF7zdqle0pGSTISiFgLUO2GMWnSJPMkvWLFiub4LVq0kGuuucYEP99++21p3769TJkyRVq1apVR+XgfAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIWyBiLUC1+6YGNm3wM3UJtUvybbfdJl999ZXccccdMm/evNSr+RkBRwRiYmLMfuzSkZ2yEwRyQKBIkSLmKFy7OYDNIRwXyJMnj9lnvnz5HN83O0QgkgL2M9cuI3ks9o2AkwJ636CfvVy7Tqqyr5wS0OtWu75rN2ISAl4SsJ+5dumlskdrWUP+lDl+/LiZVCark1yzZk355ZdfzNifgYKgCq55vv76a2nZsqWZ8CNv3ojFY6P1/EZ1vevVq2fG4ihcuHBUO1B57wnExsZK9+7dRcehIyHgNQEb+ORvutfOHOXVsep17E/uG7gWvCZQrFgx6datmwkiea3slBcB7RWqE3nZ+wdEEPCKQO3atc1cI9w3eOWMiYQcAN25c6fpuv7kk09K//79zdPGQNVt1KiR6Jf4gQMHytSpU6V06dKBskmZMmWkbdu2ooMfn3/++QHz8CYC4Qjok3A+jMKRYxs3CGhLeRICXhSwLUC9WPbUZdYZafUeJu2synofpO/pTW/qIK/+PGzYMDPUT+r98LN3BLhv8M65oqTpBbhvSG/CO94QoOWnN84TpQwsQLwhsItb3w05AKpPxps3by4DBgyQ8ePHy7hx4+SSSy5JVz+9idRu7k8//bQ0a9ZM3nzzTenatWu6fPpGiRIlZNasWQRAA+rwJgIIIIAAAgjktMDatWvl22+/zfCwgSZybNKkCQHQDMVYgQACCCCAAAIIIIBA7gmEHADVwKa26NSgpo4307lzZ7nyyivl2Weflfr1659TkzFjxsiiRYvk+++/N90y9IvB7bffbr4cVKhQweT9z3/+Ix999JFoi1ISAggggAACCCDgBgF9uLty5cp0LUD79u0rW7ZskXfeeUdq1KiRUlTtuqf3RiQEEEAAAQQQQAABBBBwn0DIAVCtQsmSJeWuu+4yLTcfffRRuf/++0WDm4MGDZLHHntMypYta2qqXwY++OADufzyy2XBggWiEyMNGTLEvLRLvL42bNhguinreHckBBBAAAEEEEDALQI6nE/aZMdA194wDRs2TLua3xFAAAEEEEAAAQQQQMCFAmHPOnTdddfJ559/bibqWLZsmbzyyivyySefmDGxtDWoTpakScf4/Omnn+T5558/Z2bC/fv3pwQ/X3755XNaUbjQiSIhgAACCCCAAAIIIIAAAggggAACCCCAgAcFwg6AxsfHy549e0yVtaXnrbfeKuvXr5e7775bHn/8cdEZuN977z2zXicGGDp0qOkypkFTbTWqLUE1KKrdy2655RYP0lFktwskJCTIjz/+KElJSW4vKuVD4BwBfYA0b9482bZt2znv8wsCXhA4ffq0F4pJGRFIJ7Br1y5z33DkyJF063gDATcLnDhxwtw36PAcJAS8JrBq1SpZuHChmQnea2WnvNEtsHv3bnPfkHbCzOhWcXftww6AalBTxwBNnYoVKyZPPPGErFu3Tjp16iT9+vWTNm3ayPz58022cuXKyVVXXWXyaKtPDYrWqlUr9S74GQHHBDQAeuDAAQk0UYVjB2FHCERA4ODBg+a61VmoSQh4TeDMmTOmyCdPnvRa0SlvlAvY+4a9e/dGuQTV95rAoUOHuG/w2kmjvCkC+sBfP381kE9CwEsCO3fuNPEG7hu8c9bCGgNUq7dx40bRgKe2sCtcuLAZB0t/11S5cmWZPHmy3HvvvTJ8+HC58MILpXfv3vLMM88Q8DRC/IMAAggggAACCCCAQM4LaA8DHb4qdfr555/Nr4sXL5YJEyakXiU6Fu7FF198znv8ggACCCCAAAIIeE0g7ACodmPXbhY6yZE+ddTu70ePHpXrr79e7rnnHhMc1QkCdAb4L7/80kyUpJMF3HHHHaYLfKlSpbxmRXkRQAABBBBAAAEEEPC0wGWXXSaJiYkB6zBjxgzRV+pUoEABc4+vQ16REEAAAQQQQOC/Anny5IHCYwJhBUA//vhj+fTTT01X96pVq6ZUWcdM0smQ2rZta8YCve2228w6nQW+W7du8s9//tPMEv/222+bIKgGQ/WmioRAJAQqVapkxv/UibhICHhJoGTJklK2bFnTmt5L5aasCKiADpGjib/vhoF/PCSg9w0aGNTPXz8nvVdfsGDBOVVMTk6W5cuXS+PGjU0jhtQrtUEDwc/UIu77uUSJEua61TkaSAh4TaBatWrmO1vBggW9VnTKG+UCcXFxokOX6VCPJG8IhBUAnTt3rtx5552SOvip1dUu8A888ICZ4Ei7vGtAdNiwYUYif/78cvvtt5txQceOHSsPPfSQ6Dig2i3+mmuu8YYWpfSUgH4g6YuEgNcEChUqJO3bt/dasSkvAkaAQAkXglcFypcvL/rye9Ix+vVF8o+ABo64b/DP+Yy2mmgvURICXhTQwCfBT2+dubAmQTp16pTExsZmWFMNhE6fPl1mzZplxgJNnbF48eLy1FNPmdaj2lK0T58+0qFDB1m0aFHqbPyMAAIIIIAAAggggAACCCCAAAIIIIAAAghkWyCsAGjHjh1N600dAzSjpC2YxowZI+PHjw+YpUqVKjJlyhQT+NTWIpdccknAfLyJAAIIIIAAAggggAACCCCAAAIIIIAAAgiEKxBWAPSiiy6SAwcOSIsWLeTf//53hsfWMYMSEhJk7969GeZp2bKl/PDDD/LRRx9lmIcVCCCAAAIIIIAAAggggAACCCCAAAIIIIBAOAJhBUB1XMXXXnvNBEGvuuoq6dmzp+i4oGnT6tWrZdeuXbJt27a0q9L93r1793Tv8QYCCCCAAAIIIIAAAggggAACCCCAAAIIIJAdgbAmQdID3nLLLfLbb7/J3//+dzPep4752axZM2nTpo3oQMa7d++WDz/80IwV2qhRo+yUkW0RCEvg7NmzcuzYMSlSpEhY27MRArkpoNeuTmpgZ9TOzbJwbATSCujf+BEjRqR92/yus0lr0okPA83oqtvVr1/f5OEfBNwkwH2Dm84GZQlVgPuGUMXI7xYBnV/kzJkzAe8Z3FJGyoFARgJHjx4l3pARjgvfDzsAqnUZN26cNGnSRO677z7Tzf0///mP6Ct1euONN0THAyUhkNMCa9asMZNttWvXjtnZchqf42VL4PDhwzJ79mypVq2a6FAiJATcJqDX6OTJkzMt1rvvvhtwff/+/QmABpThzdwWWLdunei9g07SWaFChdwuDsdHIGiBxMREM/mszrGgQ5SREPCSwPz58yUpKUm6desmOjcICQGvCGzYsEFWrVolrVu3looVK3ql2FFdzrC6wKcW++tf/2puFh988EEzm7vOAK8vbQn61VdfycCBA1Nn52cEckxAn8ZossscOzAHQiCbAvaatcts7o7NEUAAAQSCELCfuXYZxCZkQcAVAtr6UxPXritOB4UIUUB7jpw8eVK0JSgJAS8J2F5PfPZ656xlqwWorWaZMmVk7Nix5ldtvk6XTSvDEgEEEEAgI4Hjx4/L0qVL5fTp0ylZtmzZYn7WGwptEZA6xcbGml4Hqd/jZwQQQAABBBBAAAEEEEAAAQSyEnAkAJr6IAQ/U2vwc24K2GuRrhS5eRY4djgC9tq1y3D24YVt7r33XjOhXqCybt26VS688MJ0q2bMmCFdunRJ9z5vIIAAAtkVsJ+5dpnd/bE9AjklYO91uXZzSpzjOClgr1u7dHLf7AuBSArYa9YuI3ks9u2MgOMBUGeKxV4QyL5A3bp1zSRccXFx2d8Ze0AgBwXKli0rTZs2FV36OelYTzpujvYcsEl/1vF0ihcvnm4sHX2vQYMGNitLBBBAwFGBOnXqSNGiRaVy5cqO7pedIRBpgVKlSpnJaEuXLh3pQ7F/BBwXaNmypWivoAIFCji+b3aIQCQFateubSZAio+Pj+Rh2LeDAgRAHcRkV+4SiImJkZo1a7qrUJQGgSAE8uTJIzVq1Agip7ez9OrVS/RFQgABBNwgUKRIEalVq5YbikIZEAhJQO8bqlevHtI2ZEbALQJ+f+DvFmfK4bxA4cKFuW9wnjWieww5ALpo0SIZP368DBo0yEx6lFnpdDa3H3/8UX799VczO7x+uOnMhPalf6xJCCCAAAIIIIAAAggggAACCCCAAAIIIIBApARCDoC2atVKdu3aJR07dpTevXvLxx9/HLBsP/zwg9x8881iJ7RIm6lHjx7y1ltvSYUKFdKu4ncEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMARgZADoHrUkiVLmoPr2G2B0qhRo2T06NFy9uzZlNU6np2O3abdkteuXStz5841Y9y98847TGiRosQPCCCAAAIIIIAAAggggAACCCCAAAIIIOCkQFgBUFuA1AFO+97PP/98TvBTu7u/+OKL0r59e5vFLPft2ycaKO3bt6+ZBKNcuXLnrOcXBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAguwJ5s7uD1Nvr7L133nlnSsvP888/X2bNmpUu+KnblClTRiZMmCAPPfSQDB8+PPVu+BkBRwSSk5Nl48aNcvr0aUf2x04QyCkBfbi0adMmSUxMzKlDchwEEEAg6gWOHj0qGzZskFOnTkW9BQDeEtD7hs2bN8vhw4e9VXBKi8CfAnv27JEdO3ZggYDnBLhv8NwpE0cDoG+88YYsWbLEKBQqVEg++ugjKVGiRKYqQ4cOle3bt8vixYszzcdKBEIVWLdunaxcuVISEhJC3ZT8COSqwN69e2XFihXy22+/5Wo5ODgCCCAQTQLr1683vZL++OOPaKo2dfWBwP79+2X58uXm+vVBdahClAlo/EBjASdPnoyymlNdrwtoYysdFlLjWSRvCDgaAJ08eXJKrbUlaPXq1VN+z+yHrl27yk8//ZRZFtYhELKAtkjWZJch74ANEMglAXvN2mUuFYPDIoAAAlElYD9z7TKqKk9lPS1gr1m79HJltIegNqApVqxYyqtZs2amStpCO/X7+nPFihVN8NfLdY72stvr1i6j3YP6e0fAXrN26Z2SR29JszUGaGo2bf67aNGilLcGDRqU8nNWP+TNm5cnllkhsR4BBBBAAAGXCBQuXFjatm0bsDQLFiwwQ+G0bNlSChQokC5PVj1D0m3AGwgggAACUSNw6NChDLvya1f/pKSkcyx0yAr9HkpCAAEEEEAgK4GwAqCdO3eWmTNnyu+//y7PP/+8aLBTuxrbMZMaN24sderUyerYZv3x48dl4sSJ5uldUBuQCYEgBWJiYkxOuwxyM7IhkOsCRYoUMWXg2s31U0EBMhCIj48XnfQwUCpatKjoGMx6n1CyZMlAWXgPAVcK2M9cu3RlISkUAgEE9L4hT5484odrd+TIkTJs2LB0Pbi0hZU2mkmbChYsGPBhW9p8/O5eAb1uNbidP39YoQn3VoyS+V7Afubape8r7IMKhvUpM3jwYLnppptEx/x84YUXZOzYsaKzvdt08cUX2x+zXI4bN85M9lG+fPks85IBgVAE6tWrZ4Zh0JZKJAS8JBAbGyvdu3cXvaknIeA1gXz58pkiB/qi6rW6UN7oEtCH91WrVhXuG6LrvPuhttoVvFu3br4JBNoHwX44N9Qha4H27dubAKi9f8h6C3Ig4A6B2rVrizYK4L7BHecjmFKkf4wWzFZ/5ilevLjoBEY68KsGQXfv3p2ypd48BpP27dtngqeat1q1asFsQh4EghbQJ+F8GAXNRUaXCehEcnoNkxDwmgDXrdfOGOW1Atw3WAmWXhTQ+wYePHnxzFFmbfkZaMgcZBDwggDxBi+cpf9fxrADoHYX+oGlrUGXLl0qM2bMME8fa9asaVdnuly4cGHKOC6pW5BmuhErEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBIAXC6gKf0b67dOki+go2tW7dWurXr29af+qs8SQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBJAUcDoKEWrEyZMrJ69epQNyP/nwI6uUQowWbQEEAAAQQQQAABBBBAAAEEEEAAAQQQiEaBbHeBj0Y0N9RZA6AkBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhcwLEWoDoG6OLFi2X9+vXmpWODxsXFSd26deXSSy81Xd0zLwprQxH45ZdfQskelXkTEhLMtdiyZUspWrRoVBpQaW8KHD9+XBYtWmRmIw52Ujlv1pRS+1Hg9OnTfqwWdYoCgV27dsnatWtFx6XXWbVJCHhF4MSJE6JzK+hsxNWrV/dKsSknAkZg1apVZl6QVq1aMQEo14SnBHQi8DVr1kjz5s0lNjbWU2WP1sJmOwA6Z84cGTNmjOmSnRlijRo15O6775Zbb7016oJRP/30k3z66aeZ8QS97syZM7J8+XJRd1LmAhoAPXDggOzbty/qrrnMZVjrdoGDBw+a6zZfvnwmCOr28lI+BFIL6N8pTSdPnkz9Nj8j4HoBe9+wd+9eAqCuP1sUMLXAoUOHzH1Dnjx5CICmhuFnTwhs27ZNNIivr0KFCnmizBQSARXYuXOniTfofQMBUG9cE2EHQM+ePSv/93//J5MmTQqqpps3b5ahQ4eaYOn48ePNzPFBbeiDTPok9uWXX5Zjx475oDZUAQEEEEAAAQQQQAABBBBAAAEEEEAAAe8IhB0AffDBB1OCn9rdonPnzlKxYkXT7b148eIm2KdPIydMmCA62dG6detEu3Vqa7x+/frJjBkzZOLEiVHxlKdSpUoyePBgeeGFF7xzZVBSBBBAAAEEEEAAAQQQQAABBBBAAIF0AtrqnuQtgbACoJ988ok8++yz0q5dO3nqqaekQ4cOkjdv4PmUNM8jjzwi2q3o3XfflVdeeUV+++03efvtt01AVN+LhgtHA8avv/66XHvttXLbbbdJTExMWFdKcnKyzJs3Tx5++OGwto+mjTTwnJSUZALw0VRv6up9gZIlS0rZsmWlcuXK3q8MNYg6AXs/UKBAgairOxX2toDeNyQmJprPX2/XhNJHm0CJEiXMdauNUkgIeE2gWrVq5jtbwYIFvVZ0yhvlAjrnjQ5dVq5cuSiX8E71wwqAfvPNN9KzZ095//33pXDhwpnW9qKLLjLBzw0bNsgdd9xhxgAdPny4aRmq29esWVOefPLJTPfhh5UVKlQw9e/atasJHGenThpUnjVrVnZ2ERXb6geSvkgIeE1Axz9q376914pNeREwAjp2LQkBLwqUL19e9EVCwGsCGjjivsFrZ43yWoGGDRvaH1ki4CkBDXwS/PTUKZPAzTazqINO6qMtP7MKftrd6CzwOu6nJv0D/dJLL8lHH31kBorV/SxZssRm9fXygQceMDM7O1HJRo0aObEb9oEAAggggAACCCCAAAIIIIAAAggggICvBUIOgOrsrjqzdihParQbnAZNU6c+ffqYwKeODzp69OjUq3z7s3Zp7dKliyP10wmlSAgggAACCCCAAAIIIIAAAggggAACCCCQuUDIAVAd20sDeTrWQbDphx9+MON6pM1fq1YtGTt2rHz++eeycuXKtKt9+XurVq0cqRdj/DjCyE4QQAABBBBAAAEEEEAAAQQQQAABBHwuEHIAVD1atGgh9913n5w9ezZTHm0tqvlWrFghzZo1C5j3//7v/0SDgh988EHA9byJAAIIIIAAAggggAACCCCAAAIIIIAAAgiEKxBWAHTIkCEyZcoU6dSpkxnLc9euXaLBTk0nT56UjRs3mnE+dbKev//97+b9jAbm1hngX3jhBfnuu+9MPv5BwCkBDdAfPXrUqd2xHwRyVODYsWMpn6s5emAOhkA2BbJ6OJrN3bM5AhET4L4hYrTsOAcEuG/IAWQOERGBU6dOyYkTJyKyb3aKQKQFiDdEWtjZ/Yc1C3zr1q1NYPOuu+6SH3/80ZQof/78Urx4cdM1Pu2XH239qRMAZZQuuOACM67o6dOnhdljM1Li/VAF1qxZI+vWrRMNxDM7W6h65M9NgcOHD8vs2bOlWrVq0rx589wsCsdGIGQB/VuuyT4YDXkHbIBALgnoPYPeO7Rt21YqVKiQS6XgsAiELpCYmCizZs2SKlWqmJ56oe+BLRDIPYH58+eb4fK6detGLCD3TgNHDkNgw4YNsmrVKtH4WMWKFcPYA5vktEBYLUC1kHfeeae88sorZjxQ/V2f3OjkSIGCn5988okUKVJEs2WYOnfuLHoBkTIX+Mc//iHVq1eXwYMHZ56RtSmtP3kqw8XgNQF7zdql18pPeaNbwN4H2EBodGtQey8J2M9cu/RS2SlrdAto609NXLvRfR14tfbJycmmF6nGE0gIeElAr11NfPZ656yFHQDVKt5+++2yfv16efTRR+XSSy+VqlWrinZp1+j3JZdcIm+88YaZ6V0nO8oq6UzwNWrUyCpbVK/XiacefPBB2bp1q0ycOFHmzZsX1R5UHgEEEEAAAQQQQAABBBBAAAEEEEAAgawEwuoCn3qnJUuWlCeeeCLlrXC7sevM8qTMBWJjY6Vy5cqyefNmKVCggAQTWM58j/5emzfvf+P7DKvg7/Psx9rZa9cu/VhH6uR/Aa5f/59jv9XQXrN26bf6UR//Cth7Xa5d/55jP9fMXrd26ee6Ujd/Cdhr1i79VTt/1ibbAdC0LPYPcNr3+T37Avofa86cOfLBBx9Ily5dGGciC9K6deuKBo3j4uKyyMlqBNwloA+EmjZtmjLEiLtKR2kQyFzA3gfYZea5WYuAewTq1KkjRYsWNQ+b3VMqSoJA1gKlSpUSnXOhdOnSWWcmBwIuE2jZsqUcP37cNPBxWdEoDgKZCtSuXdsM9RgfH59pPla6R8DxAKh7qubPkujg5vfdd58/K+dwrWJiYqRmzZoO75XdIRB5AR1KhCFBIu/MESIjwFPwyLiy18gL6Hj19K6JvDNHcF5A7xuq/zlHAAkBLwrQE9SLZ40yq0DhwoW5b/DYpZCtMUA9VleKiwACCCCAAAIIIIAAAggggAACCCCAAAJRJkAL0Cg74eFUd/bs2fLOO++Indk3nH3YbXQCJ027d++WpUuXSrFixUS7nAVKO3bsMPnsOm1aXq5cOftryvLMmTOyZs0a03VC39Rul9r9XZ/IpE1HjhyRDRs2pNSF4+PP9cf/v7SfE/p7pD5/li9fbg6n42XrJIJ+vP4y+lvB5y9/f/j7y/2H/Xzg/ov7Lz/+/eP7B9+/+P7J9++03ysicf+b9hj8HrwAAdDgraI25wsvvCDTp093tP4JCQmybds2s0/tsqOTOqVNa9eulcTExJS3jx07FjAAmpSUZAIJKRn//EEn56patWrqt8zP27dvFxuEtSs5Pv5cf/z/s58Hdhmpz5/ff//dHEIf3Pz222+my6Lfrj8b4LCWdsnnL39/+Pv734fA9v8E9x/cf/jt85/7b75/8P2L75/2b5xdcv/n/P2ftWUZukCeP7+onA19M7ZwQiA5OVm++uor0yJRPxi0xdHhw4elTJkyZvITvTHu1KmT6MDQuTmZhH5h//7770W/sGc3Pf744ybwqZM4vfHGG6YFqNY3UNLg5/79+80qHdtIx4fRcT0DpV27dokGSDWpVaVKlSTQOHQnT56UnTt3ptRFWyBwfPwDXVNcf/z/i8Tnj7aAa9iwoZloRYNBfvz80frp/59Dhw6Ziejs/y+/fP42atTIBK9XrVplziV/f/j7y/0H91/cf3L/zfcPvn/x/ZPv35GMP/zlL3+RqVOnypQpU6Rfv3729ppliAIEQEMEcyL7rFmzZPLkyTJt2jTRJtFZJZ3JvE+fPjJixAjTtTur/G5e36JFC9P1vVevXqb+kSyrBpi1pakGknMzgBzJOrJvfwroc6nNmzebFs/Fixf3ZyWjtFYaGNTPdD2v+sDLj0nrpn/b0gZA/VLXtAFQv9SLeogcPXrUPIzW+4b8+ekkxTXhHQG9b9iyZYt5qKZ/Y0gIeElgz549cuLECalcubKXik1ZEcjR+wYCoM5ccEyC5IxjUHvRcS979+4tnTt3NpH7YIKfumP9kjxp0iRp0KCBDBkyRE6dOhXU8aI907p162TlypUmCBrtFtTfWwJ79+6VFStWmFZm3io5pUVARMc31WSXmCDgFQEdl1db9v7xxx9eKTLlRMAIaI8FHWNar18SAl4TWLJkiSxevFi0pwgJAS8JbNy40Xzuam9ekjcEXPF4W78kzZgxQ7p37+4NtTBKqQGNjh07io4rp0nHR2nSpImZAKNKlSqma7d279aBk7UFgrYS0pcGP3WbhQsXmlYJr776quh/tC+++EIKFiwYRkmiZxPbZd8uo6fm1NTrAvaatUuv14fyR6cA1290nncv19pes3bp5bpQ9ugSsNesXUZX7amt1wXsdWuXXq8P5Y8eAXvN2mX01Ny7NXVFAFSDn7/88ouvA6ADBw40Y33eeeedcuutt0rTpk1Dvmq0RcLTT9J6kZ8AAEAASURBVD8tL7/8skyYMEGGDRsW8j7YAAEEEEAAAQQQQAABBBBAAAEEEEAAgWgSiEgAVJuv//rrr6b7sXbFOHDgQEBTzafdwn/88Ud56KGHAubxw5urV6+Wb7/9VmbPni0dOnQIu0o6sPKLL74oV1xxhdxwww1y7733BpzoJ+wD+GxDO2GSXfqselTHxwJFihQxtePa9fFJ9nHVdNI6TYy97OOT7NOq2c9cu/RpNamWDwX0vkE/e7l2fXhyo6BKet3qOLaMvRwFJ9tnVbSfuXbps+r5sjqOB0D37dtnxrmcM2eOL8HCqdSCBQtk0KBB2Qp+pj5u165dzYzoOklKrVq1Uq/i51QC9erVE53IQIcVICHgJQGdwECHBGGYCy+dNcpqBWzgM9BMmDYPSwTcKFCnTh0zRBH3DW48O5QpM4FixYpJt27dpECBApllYx0CrhRo3769CYDa+wdXFpJCIRBAoHbt2hIfH0+8IYCNW99yfBKkG2+8UQh+nnu6tZWrzhrrZGrTpo0w2G7movoknC8xmRux1r0ChQoVMq053FtCSoZAYAHbAjTwWt5FwL0C3De499xQsqwF9L6BB09ZO5HDfQLa8pPgvfvOCyUKToB4Q3BObsnlaAvQ48ePy9y5c03d+vXrZ7pqt2rVSsqXLx+wvjqb+a5du+Spp54KuN4vb+pTASeDwtpFYNmyZdKsWTO/EFEPBBBAAAEEEEAAAQQQQAABBBBAAAEEIiLgaAB0y5YtcuzYMTPb+ZQpU4IqcMmSJeWZZ56RiRMnBpXfi5m0teaAAQOkR48e0qtXr2xX4fHHHzdPydSOhAACCCCAAAIIIIAAAggggAACCCCAAAIZCzjaBb5atWqm60WTJk0yPmKANXFxcXL77bcHWOOPt3Sczquuukquvvpque666+T7778Xbf0aSjp9+rTMmDHDjO+jAdDbbrstlM3JiwACCCCAAAIIIIAAAggggAACCCCAQFQKONoCVMc/uPbaa0VnPQ81+X3Mmtdee01WrlwpH330kXmVKFHCjAuqA+fqRD1FixY1MzfqLI4aHE1KSpLk5GTZsWOH8VyxYoXs37/fsI4cOVIGDx4cKjH5EUAAAQQQQAABBBBAAAEEEEAAAQQQiDoBRwOgqvfkk09Ku3bt5KeffjLLYER1HFANEI4aNSqY7J7MU7ZsWZk9e7aZDf6LL76QQ4cOGSN1CjZpcHTYsGEyZsyYYDeJ6nwJCQmyfv16admypQkwRzUGlfeUgI6nvGjRIjMbcdWqVT1VdgqLgPZYICHgRQG9H127dq20aNFCdFZtEgJeEThx4oQsXLjQzEasDStICHhJYNWqVabxj84dwkSKXjpzlFUnu16zZo00b95cYmNjAfGAgKNd4LW+2t3722+/NV3aN27caMYE1XFBA70SExNF8zz44IOiE/v4PVWsWFGmT58uX3/9tfTt21eKFy8eVJXLlSsnjz32mGzdupXgZ1Bi/82kAdADBw7Ivn37QtiKrAjkvsDBgwfNdastwEkIeE3gzJkzpsgnT570WtEpb5QL2PuGvXv3RrkE1feagDas0Ptd7hu8duYorwps27ZN9PNXA/kkBLwksHPnThNv4L7BO2ct5Bag2r1dn85klbQFk3bvDjb5ufVnWoPu3buLvjQoPHPmTNNKUT/09T/Q4cOHRQOl2upLX1WqVDHe2vqThAACCCCAAAIIIIAAAggggAACCCCAAAKhCYQcAG3QoIGUL19eNm/eHNqRyJ1OQMdMveKKK9K9r2/oEzBtUl2oUCEh+BmQiDcRQAABBBBAAAEEEEAAAQQQQACBHBdgyIYcJ8/2AUMOgOoRNWj38ssvS9u2bc0EPuGeeO0ep0G++fPnZ7siftvB0qVLjW+bNm3kl19+8Vv1cqQ+lSpVMuPJlClTJkeOx0EQcEqgZMmSouMGV65c2aldsh8EckzATmpYoECBHDsmB0LACQG9b9DhmfTzl4SAlwR0clW9buPj471UbMqKgBGoVq2a+c5WsGBBRBDwlEBcXJzo0GU6ZCHJGwJhBUAvv/xyM0i8jvXpRNJxMZcsWeLErtgHAikC+oGkLxICXhPQlt/t27f3WrEpLwJGIF++fEgg4EkB7eGkLxICXhPQwBH3DV47a5TXCjRs2ND+yBIBTwlo4JPgp6dOmYQVAO3UqZOjg2z36NHDPPXxFh2lRQABBBBAAAEEEEAAAQQQQAABBBBAAAG3C4Q1C7y2TrrlllsC1m3Lli2iEyAFm+6++245evSo3HjjjcFuQj4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAogbACoJnt+YILLhAdvzLY9Je//D/2zgNMiuJp40XORz7JOYtklIwgSUURCSJJUEBUEFERMyCCAVFAkCSCgggqIEmJokj4k3OOInDknDnYb9/263V3b29vw2yY3bee525mZ3q6q38zO9tTU13VQZo3b66S/nh6DMuRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgCcEDDeAetKofZnKlSurJEhjxoyx38x1EiABEiABEiABEiABEiABEiABEiABEiABEiABEvCbQEgNoOfPn5fBgwerKfNLly71uzOsgATsCVgsFhVewX4b10nALARu3Lghd+/eNYu61JMEbARw76WQgBkJcNxgxrNGnTUBjhs0CS7NRiA+Pp6zQc120qivjQDCOVLMQ8CnJEj23XvqqadkxowZtk137tyR+vXrS8qU/1adLFkycfWHH+mrV6/ajsucObNtnSsiMTExgmRTzIrn+9Wwe/du2bt3r9SoUYPZ2XzHyCNDQODSpUuybNkyKViwoFSoUCEEGrBJEvCdAMYBEBrwfWfII0NDAGMGjB2qVasm99xzT2iUYKsk4AOBy5cvy++//y758+eXSpUq+VADDyGB0BFYuXKlsgs0btxYUqRIETpF2DIJeElg//79smPHDrn//vsld+7cXh7N4qEg4LcBdPr06TJ8+HDp06eP3L59W/XBWyt4hgwZEk2qFAoo4dBm6dKllQEkHHQxqw76OtRLs/aDekcfAX3N6mX0EWCPzUxAe4BqQ6iZ+0Ldo4uAvufqZXT1nr01MwE4lkB47Zr5LEav7teuXVN2BHiC0gAavdeBGXuOaxfCe695zp7fBlB0tVevXlKlShVp2rSpXLhwQSpWrKg8GN1hSJ48uXpLWbx4cWnRooXA4EchARIgARIgARIgARIgARIgARIgARIgARIgARIgASMJGGIAhUI1a9aUefPmSaNGjeSrr75S04eMVJR1kYC3BGBkh/BNorfkWD7UBPS1q5eh1oftk4AvBHj9+kKNx4SSgL5m9TKUurBtEvCGgB7r8tr1hhrLhgsBfd3qZbjoRT1IICkC+prVy6TKc3/oCRhmAEVXYASdPXu2T/EP4uLifDou9AipQbgSKFGihPJEzpUrV7iqSL1IwCWBHDlySLly5QRLCgmYjYB+ENdLs+lPfaOXAGYlISxT3rx5oxcCe25KAlmzZpXy5ctLtmzZTKk/lY5uApUrV1ZJkVOlShXdINh70xEoVqyYpEuXTvLly2c63aNVYcOzwDdo0EAl7vAG6MWLF2XcuHHeHMKyJJAkgfTp00uRIkXoAZokKRYINwJIHFe4cGHJlClTuKlGfUggSQJ8C54kIhYIUwJ4iClatCjHDWF6fqhW4gQwbihUqFCSIcgSr4F7SCB0BPDCny+eQsefLftOIG3atGrcoBOA+14TjwwWAcMNoN4qjmQJMH7qpAneHs/yJEACJEACJEACJEACJEACJEACJEACJEACJEACJJAYAZ+mwL///vvyxx9/yHvvvScNGza01Y0ESJgG72nmV2SNP3PmjFy6dEn69etnq4crJEACJEACJEACJEACJEACJEACJEACJEACJEACJGAEAa8NoFu3bpWBAweqtl977TXBZy1ZsmSRPHnyyJIlS/QmLkmABEiABEiABEiABEiABEiABEiABEiABEiABEggZAS8NoAiLl3mzJkFcTsrVKiQQPGOHTsqAyjiKMEYirgIiQk8QE+fPi3nz59PrAi3kwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDPBLw2gCIpx8GDB2Xz5s1St27dBA03a9ZMYmNjZePGjR4HMx4yZIhcvXo1QV3cQAL+ELh27ZrExcWpoPDMRuwPSR4bbAKIiXzo0CHJmTMnEyEFGz7b85vA3bt3/a6DFZBAKAhcv35djh07psYNTGgQijPANn0lgHHD4cOHJXv27EyE5CtEHhcyAnCIunXrlse2g5ApyoZJwIkAxw1OQEzw0ackSNmyZZP69eu7zJIZExMjnTp18uoG1rlzZxOgoopmI7B3717Zvn27MoKaTXfqG90EEBt527ZtsnPnzugGwd6bkoCOA66XpuwElY5KAvv27ZMdO3bI8ePHo7L/7LR5CZw7d06FJcP1SyEBsxGA49T69esFs0MpJGAmAgcOHFDjhqNHj5pJ7ajW1ScDaFLEevbsmVQRh/05cuSQli1bOmzjBxLwl4D2QtJLf+vj8SQQLAL6mtXLYLXLdkjASAK8fo2kybqCQUBfs3oZjDbZBgkYQUBfs3ppRJ2sgwSCRUBft3oZrHbZDgn4S0Bfs3rpb308PvAEAmIAzZcvn9ealy1b1utjeAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJuCNguAGU7r/ucHNfMAmkT59eNaeXwWybbZGAPwSQRA7Ca9cfijw2VASSJUummmbs5VCdAbbrKwF9z9VLX+vhcSQQbAIYN+Dey2s32OTZnhEEcN2mSpVKGHvZCJqsI5gE9D1XL4PZNtvyjYDXSZDcNYN4X/Xq1ZNp06ZJ5cqV3RXlPhIIOIGSJUuqRAZp06YNeFtsgASMJIBYyk2aNJHUqVMbWS3rIoGgENCGz+TJDX/HGhT92Uj0EihevLgUKFBAOG6I3mvArD3PmDGjNG7cWBmRzNoH6h29BGrWrClI5KXHD9FLgj03G4FixYoJZj9z3GCeM2f408n+/fulSpUqUrt2bZk6darcvHnTPDSoaUQRwJtw3owi6pRGVWfSpEmjvDmiqtPsbEQQ0B6gEdEZdiKqCHDcEFWnO+I6i3EDXzxF3GmNig7B8xMeoBQSMCMB2hvMddYMN4DmzZtXRowYIVWrVpW33npLWcT79OkjMIxSSIAESIAESIAESIAESIAESIAESIAESMBXAv3791cGf7y44h8ZRMM1MGXKFPV1OXLkiK9fGx5nJWCoARQX3rvvvivIAv/555/L4cOHZfLkybJ3714pXbq0NGzYUGbMmCHx8fGETwIkQAIkQAIkQAIkQAIkQAIkQAIkQAJeEVi1apWaNu/VQSxMAhFA4J9//omAXoSuC4YaQDHtonv37rbewCCKOHazZ8+WgwcPSrVq1aRHjx6SP39+ZSj9+++/bWW5QgIkQAIkQAIkQAIkQAIkQAIkQAIkQAKeEFi0aJEyhCKGKP/IIJKvgfbt23vylWCZJAgYagB11xaMngMHDhS47A4fPlwmTZokRYoUkaZNm8q8efPk7t277g7nPhIgARIgARIgARIgARIgARIgARIgARIgARIgARLwmkDQDKDQ7NixYzJkyBB555131DqMnvPnz5cWLVrIxx9/7LXyPIAE3BGIi4uT5cuXy9WrV90V4z4SCDsCSB63YsUK9cIo7JSjQiSQBIE7d+4kUYK7SSA8CZw8eVKNG65cuRKeClIrEkiEwK1bt9S4AeHHKCRgNgI7duyQtWvXckq72U4c9SUBExJIaaTOeOj58MMPpV+/frZq8YM8d+5cmTBhgsBF3f7BqGjRotK1a1d59tlnJWfOnLZjuEICRhCAAfT8+fNy9uxZyZAhgxFVsg4SCAqBCxcuqOs2RYoUUqBAgaC0yUZIwCgCekbH7du3jaqS9ZBAUAjoccOZM2ckY8aMQWmTjZCAEQQuXryoxg0IP1aoUCEjqmQdJBA0ApghCpsB/tKkSRO0dtkQCZBA9BEw1AAKfD/88IO0a9dO/QhPmzZNkK0KA0ktKVOmlMcff1yef/55lRQJP9QUEiABEiABEiABEiABEiABEiABEiABEiABEiABEggEAcMNoHv27JHixYsn0BVeTPD2fO655yR37twJ9nMDCZAACZAACZAACZAACZAACZAACZAACZAACZAACRhNwHADqLOCMHZiSlHDhg2lTZs2NH46A+LngBHIkyePiv+ZPXv2gLXBikkgEASyZMkiOXLkkLx58waietZJAgElkDz5v+HFU6VKFdB2WDkJGE0A44bLly+r+6/RdbM+EggkgcyZM6vrNl++fIFshnWTQEAIFCxYUD2zpU6dOiD1s1ISIAES0AQCkgQJBqf3339fJfA4fvy4LFu2TI4ePSqlSpVSRtDNmzfr9rkkgYARyJUrl9SuXZvxPwNGmBUHigDiH9WsWZPxPwMFmPUGlABi11JIwIwEYmNj1biB8T/NePaiW2cYjjBugCGJQgJmI1CmTBmpWrWqMDSe2c4c9SUB8xEw3ABarFgx2bZtmwwYMEDy58+viDz44IOyYMECWb9+vcAzBDe4Rx55RP766y/zEaPGJEACJEACJEACJEACJEACJEACJEACJEACJEACpiFgqAEUb22QAT6xGJ8VKlSQqVOnCuKEnj59WurUqSO1atWS+fPnmwYYFSUBEiABEiABEiABEiABEiABEiABEiABEiABEjAPAUMNoPDubN++faK9P3v2rAwdOlQefvhh5Q2KgitXrpTHHntMeYwmeiB3kAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAPBAxNgmSxWGT16tVSo0YNmyrY9scff8jXX38tM2bMkJs3b9r2IVZo586dpVu3bi4zx9sKcoUESIAESIAESIAESIAESIAESIAESIAEIpDARx99JD/88INXPVuyZIkgfnW4Cmb6YgYwQiTaCxJl5syZU+mOGLDNmzeXkiVL2hdR67NmzZIvvvhCLly4oD5jxvHjjz8unTp1kqJFizqUHzx4sMybN0+uXLkiWbNmlTfeeEMeffRRhzL8QAKGGkDv3r0r77zzjsyZM0f+/vtv+fnnn2Xy5Mly8OBBB9KY9t69e3dp2bKlINkHhQQCQQDG9xs3bki6dOkCUT3rJIGAEsC1i6QGOqN2QBtj5SRgIAHceykkYEYCHDeY8axRZ02A4wZNgkuzEYiPjxfYEaI9CzxsJggTOGjQIKlcubJky5ZNfvnlF3n55ZclX758ytEM3/MTJ04o57Jvv/1WGfvC2QAKAyT+YOTctWuXCn84d+5cuX37trIR7dy5Uxk433rrLalUqZJ89dVX8sADD9guYRhGq1SpImXLlpVLly5JvXr1ZODAgbb99itvv/224Hf8gw8+kC1btqgE3Pb7uU4CIGDoFHhUCG/PLFmyyH333aemtWvjZ+bMmaVHjx6yfft2lfyoXbt2NH4CGCVgBHbv3i2LFi1SPyQBa4QVk0AACOAHfuHChbJ169YA1M4qSSCwBO7cuaMawMMMhQTMRGDv3r1q3HDy5EkzqU1dSUAuX76sxg2bN28mDRIwHQGExIMnox4/mK4DBil8/fp1GTlypDz77LNSvnx5lVAaRlBIypQplREUCafhTDZx4kRBfhUYREMhcHYbM2aMx01r787SpUsrWxG8P2HoxGxg3LcmTZqkjJaYSTx27FiHepFYu1evXmobyl67ds1hv/0HeIp26NCBxk97KFx3IGC4ARS12z/0IOP7hAkT5Pjx4/Lll1/Kvffe66AAP5BAoAjgRwSil4Fqh/WSgNEE9DWrl0bXz/pIIJAEtAdotD/IBJIx6w4MAX3P1cvAtMJaScB4AtoIwmvXeLasMfAEYNCCRyA8QaNZUqRI4fGUbUwFb9OmjUN4wWCxu3XrlrRu3dorJyMYcBMTzHZ75plnpH///sqOBKe55cuXOxSHARSzOs+fPy9Tpkxx2Kc/wAY1ffp0eeGFF/QmLkkgAYGAGEBxceLNxYYNG2Tt2rVqPX369Aka5wYSIAESIAESIAESIAESIAESIAESIAESiGYCmNKeNm1ajxH07dtXKlasaCt/+PBhmxF5x44dylgIJzRsx9/FixdVWRgw9TYsXXlUwpj4119/KXsOjNO6DPK5wPgJG8/Vq1eVEVTXa1PExxVMYa9Zs6bqg/M0d+SOgZEUMmzYMDXV3bmZ33//XXLkyKHCBzjv42cS0AQMN4CWKFFCxXOA1yfiOFBIIFQEdOxEvE2jkICZCOhrVy/NpDt1JQFNgNevJsGlWQjoa1YvzaI39SQBPdbltctrwYwE9HWrl2bsQ6h0hhESOVeaNGmikgIhPijWETMTthgYOJs1ayaFCxe2TVmHERNh4jBTF9t//fVXB/VHjRolXbp0UbMo4dBWoEABZXREIRhp9+/fr8rPnj1bTWEfMWKEw/G+fsD5f+SRR9ThS5culbi4OIeqevfuLfB8RSxRZ51RENPou3bt6nAMP5CAM4HEfZGdS3rwGRct3I5z5crlQWkWIYHAEoAxPiYmhtdjYDGz9gAQwNvLcuXKqbeYAaieVZJAQAnoB3G9DGhjrJwEDCRQvHhxyZAhg+TNm9fAWlkVCQSeADIeI2agjhcY+BbZAgkYRwAJf2CUQ2ZwincEkPEc3JCHBV6b/fr1U0ZA3AvAFDE1kXvFPq9ApkyZpFu3brJq1Spl0LRvEbGwkXQJS51lHXXp43EcPEh79uypjJ/ItG6kII8MBOGUYGjNnTu3rXo82z/22GMq4fbQoUMdwgUgf8KCBQsExlsKCbgjYKgHKCzyCMZLIYFwIICwC0WKFBE+hIfD2aAO3hDAvRRvZDFAoZCA2QjQg8NsZ4z6agII4YQHPo4bNBEuzUIA44ZChQqpF/9m0Zl6koAmgBf/fPGkaXi3vOeee1QsUCRHgjRq1EhatGghU6dOlRkzZqhtSEbtSvDixFkOHDigDKlr1qyx7YIHKc6Rs+C+Y7QgSZIWVwkJX3vtNbV72bJlsmnTJl1UOeE1bdpUEuurrSBXop6AoQbQqKdJACRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQJAIwhELq1KnjV4sPPvigepGCeJsffPCBmgYPD1N4hQZD/vnnH1sz8Ph0FvSvSpUqavPnn39u2z1x4kROf7fR4Io7AjSAuqPDfSRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQ4QQwE2LcuHEqGROm0yM0zOLFi132OhAeoIg5CkE4mnvvvddlu6+++qrajtCLx44dkz179siFCxdUAiWXB3AjCdgRoAHUDgZXSYAESIAESIAESIAESIAESIAESIAESCAaCTz11FOyceNGqVatmjIwIqnS8OHDE6AIpAH0/vvvTzQcTatWrSR//vyC7PRIwDTJmvwISZsoJOAJARpAPaHEMiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQoQSQ/AiZ5eH5iSRJY8eOVYbIt99+Wy5fvuzQa6MNoIjrOWvWLOX9OWzYMIe27D+kTJlSevXqpTbBW3XatGnSsWNH+yJcJ4FECdAAmiga7jA7gWvXrgkCOd+5c8fsXaH+UUYAmQ8PHjyYYKARZRjYXZMSQBZSCgmYkcD169dV1tn4+Hgzqk+do5gAxg2HDh0SZEKmkIDZCJw+fVp5GppN72Doq3+P/HmexXRyyNGjRx1UPnfunPqMrO5akFho9uzZ6iMMnMj6PmDAAMFz9bZt29T2tGnTqqU395ukxoZbtmyRJ554QmWuh0dnuXLltEoul127dlXJYjH1Hd6irpI0uTyQG6OeAA2gUX8JRC4AvMHavn27xMXFRW4n2bOIJHDmzBk1yNi5c2dE9o+dimwCepCul5HdW/Yukgjs27dPduzYIcePH4+kbrEvUUAAhoytW7eq6zcKussuRhgBTLdev369mtIcYV3zuztHjhxRdcBIfPPmzUTrQyxMCJ59naV8+fJqEzwlFyxYIKtXr5aePXuKzrKO7fbHjRw50qGt5MmTS2xsrM0oWbRoUVUfsszv379fvv/+ezl//rxzsw6ftfHV/vcV48Rdu3ZJnz59BMmXkGxp/Pjx0rJlS4djXX2IiYmxTXuHMZRCAp4SoAHUU1IsZzoC+k2TXpquA1Q4agnoa1YvoxYEO25qApF0/WKQvnTpUnn99ddFZyh9//33ZfLkySrwvqlPFJW3EdDXrF7adnCFBMKcgL5m9TLM1aV6JOBAQF+3eumwM0o/rFu3Tk3zHjp0qCJw48YNady4sQwePNjBUAxnCRgAkQgI8tprr8lHH32k1vW/++67T1BPihQp5OGHH5ZXXnlFHn30UXnkkUekVKlSUqlSJbFnjxcqjRo1ktGjR0v//v3lt99+kzlz5kjGjBlVlTVq1JCqVasqoymW2bNnl6xZs+rmHJbz58+XNm3ayNq1a9V2fM6VK5cULlxYJTmCvvBy/fjjj9XMTW9ieWIaPKbqP/TQQw5t8gMJuCOQ0t3OQO3DF7pkyZICyz2FBEiABEiABEiABMKVAAyfGGTDM9Be4PmAP4xl3nrrLXnjjTcEXhIUEiABEiABEiABEvCHAAyL+HOVfMi+3jJlyiivSXhOuhNkTn/55Zfl1KlTkidPHlW0du3aapv9cc2bNxckQYK3KcJqZMqUSRlB7cukSZNG1qxZo8J1FSxYUBCTMzGBoRV/8DI1WtD2kiVLxOhYpEbryfrCi0DARuoXL16UTz75RNq1a6ceCuy7jRhLFStWlM8++8x+M9dJwFAC6dOnV/XppaGVszISCCCBdOnSqdp57QYQMqsOGAE9EIWngdnlyy+/VF4QMH4WKVJE3n33XcmXL5/qFoye9evXVzH3sN60aVPB+IZiXgL6nquX5u0JNY82Ahg34N7Lazfaznxk9BfXLaY/uzOkRUZPQ9sL8NXGT2iiY4Paa5U6dWr1EUZOeIfmzZvXfrdtHfcbTIUP9TkrUKCATSeukIAnBBI313tydCJl4C3RqVMnW6DdZs2aOZSsU6eOcqXG2wAE2v3mm28EXzIKCRhJAF7GhQoVEh2o2ci6WRcJBJIAPMqaNGkiehASyLZYNwkYTUAbPs3uDfnzzz/bsowiAQCMnHhAmzlzpkLWvn17NRUNY562bduqcU3nzp0D4uVg9Dlifa4JYCodHqY4bnDNh1vDlwCmpmJ6LO5RFBIwG4GaNWsKEnnp8YPZ9Ke+JEAC5iFguAcoAvUig5cOdJsYihIlSsiff/4ps2bNUg8ViZXjdhLwlQDeTPEhxld6PC7UBPBSSHvShVoXtk8C3hCIhOsWmU1feOEF9UA2bNgwQbzPxAwLiD2F8UzmzJll+vTpKk6WN7xYNnwIcNwQPueCmnhPAOMGs7948r7XPCISCMCLMLHf2EjoH/tAAiQQPgQMN4AivsSVK1fUFAwEz23dunWivYULNqbII7aFDoybaGHuIAESIAESIAESIIEgEBg3bpycOXNGZSVFzKykBNPEkJgAMmjQoKSKcz8JkAAJkAAJkAAJkAAJkECQCRhuAEUgWmQSgwfowoUL1bQwd30qW7asyjo2depUd8W4jwRIgARIgARIgASCQuCXX35R7SBTqqfy3HPPqQypeKF77NgxTw9jORIgARIgARIgARIgARIggSAQMNQAevz4cUHyo5EjR0rWrFk9Uv/8+fOq3NatWz0qz0IkQAIkQAIkQAIkEEgCOuM7YpZ7Kph+Wq1aNVV8586dnh7GciRAAiRAAiRAAiRAAiRAAkEgYKgBNDY2VpCFsFy5ch6rvmrVKlX2woULHh/DgiRAAiRAAiRAAiQQCAJ3794VjEkQS8/Tl7laj+zZs6vVc+fO6U1ckgAJkAAJkAAJkAAJkAAJhAEBQw2gCGBcsWJF2b59u0ddwxSzxYsXq7JVqlTx6BgWIgFPCcTFxcny5cvl6tWrnh7CciQQFgRu3rwpK1asECSVo5iXwOzZsyV//vySO3du21+xYsVUhxAr23471gsXLiz6paB5ey1y584dM6uvDJ/ZsmVT4XkQB9QbOXnypCqeM2dObw5j2TAhgPOHcQO+nxQSMBOBW7duqXHD4cOHzaQ2dSUBRQCzLhA+BpngKSRAAiQQSAKGGkChaMuWLaV///5y48YNt3rjwbBTp062MnramG0DV0jATwIwgCLEwtmzZ/2siYeTQHAJwPsM1y3jCAaXu9GtIRY2/k6cOGH7O3XqlGoGg3z77Vj/+++/RRvQjNYlmPXBgxJy+/btYDZraFvly5dX9S1dutTjevGybc2aNcqAivjmFPMR0OMGbw3f5uspNY40AghBxnFDpJ3V6OkPXvjj/gtDPoUESIAEAkkgpdGVI2HAX3/9JYib1b17d0mWLJlq4vLly7Jnzx7ZtWuXjBkzxsHLpUGDBtKxY0ejVWF9JEACJEACJBAyAi+99JK0adMmgSEQA3xMr8asCXtBDElvp1zbH8914wi0aNFCli1bJkOHDpXWrVvbxjLuWhg1apRcv35djX8QEohCAiRAAiRAAiRAAiRAAiQQPgQcn74M0AsGz2+//VZatWolyIgKwUNeTEyMy9qRMf7nn39O8CDosjA3kgAJkAAJkICJCOiYkCZSmapaCTz77LPy8ccfy7p169SslgEDBrjlosuhUL9+/dyW5U4SIAESIAESIAESIAESIIHgEzB8Cjy6kClTJlmwYIH89ttvgmlk8fHxCXpWsmRJmTJlinq4yJw5c4L93EAC/hLIkyePII4bDRD+kuTxwSaQJUsWyZEjh+TNmzfYTbM9EvCbALxbIalSpfK7rlBVgISOEydOlBQpUsgHH3wgPXr0EMxkcSU//PCDPPTQQ8r784UXXpD69eu7KsZtJiCgxw24/1JIwEwE8CyF6zZfvnxmUpu6koAiULBgQcH9N3Xq1CRCAiRAAgElYLgHqL22TZo0EfzhoWH37t2CwNxI9FCiRAnh9DB7UlwPBIFcuXIJ/igkYDYCmApds2ZNs6lNfUlAEYDRMBIE4Xm+++475Q2K6e3Tpk0TTI3XGd5HjBihwvls27ZNdbdDhw6CbRTzEsDYlONT856/aNYchiOOG6L5CjB338uUKWPuDlB7EiAB0xAw3AMURk5kMLYXeIRWrVpVTYuvVauWw+Dy5ZdflkuXLtkX5zoJkAAJkAAJkAAJhJxA27ZtZfXq1VK3bl2VYGTcuHEqeRUUGzt2rMD4Ca+VCRMmKGOpc1zXkHeACpAACZAACZAACZAACZAACSgChhtAq1evLps2bfIYLzwmmjdvzqxvHhNjQRIgARIgARIggWARqFixovzxxx/K2Pnpp5/aElV169ZNhfs5dOiQ8hINlj5shwRIgARIgARIgARIgARIwHsChhtAvVWhcuXKsnLlSpUZ3ttjWZ4ESIAESIAESIAEgkGgbNmy0qdPHxXKB+316tVLGjduzJhlwYDPNkiABEiABEiABEiABEjATwIhNYCeP39eBg8erKbML1261M+u8HASIAESIAESIAESIAESIAESIAESIAESIAESIAEScCTgdxKkp556SmbMmGGr9c6dOyoDqo6DlSxZMnH1d+PGDbl69artOGaCt6HgikEELBaL4DpDNl8KCZiNAK5dJDXQGbXNpj/1jV4CuPdSSMCMBDhuMONZo86aAMcNmgSXZiMQHx8vd+/e5YwKs5046ksCJiTgtwfo9OnTZejQoeohHcZPyPXr11Xmd2R/R4KjixcvyoULFwQen8ieevbsWQfjZ4YMGRg/y4QXT7irvHv3blm0aJGcPn063FWlfiTgQAD3zYULF8rWrVsdtvMDCZiBgB4L4GGGQgJmIrB37141bjh58qSZ1KauJKCeuzBu2Lx5M2mQgOkIIBzekiVLRI8fTNeBMFT44MGDIdcKz+A7d+4MuR5UgATsCfjtAYrKEAerSpUq0rRpU2XoRMKAmJgY+3YSrMOrKX/+/FK8eHFp0aKFlC5dOkEZbviPwLVr1yR9+vT/beBakgRgiIfoZZIHsAAJhAkBfc3qZZioRTVIwCMC2gOUDzIe4WKhMCKg77l6GUaqURUScEsA3p8QXrtuMXFnmBLAc+7t27cFnqApUqQIUy29V+vYsWMyfPhwQag/9C8pqVevniqfVLnE9u/bt0/Gjx+vEjRiPRT3g5s3b8rIkSPVDOE1a9ZIjx49/OpTYn3ldhLwlYAhBlA0XrNmTZk3b540atRIvvrqK6lWrZqvOvE4FwS2b98ugwYNkoceekg6dOhgy0Lroig3kQAJkAAJkAAJkAAJkAAJkAAJkAAJhIhA3rx55dNPP5XPPvtMJVGEGvv37xds13LlyhU5fPiwvPfee4JZCP5IkSJF5JVXXpFhw4ZJmjRp/KnK52MRvqt3795qBubq1at9rocHkkCgCBhmAIWCMILOnj3bliHVE6VPnDgh2bNnl1SpUnlS3PRl8Pbnf//7n2zZskWFBrj33nsFHrMFCxZ027f7779fvvnmG+Vp27dvX2nVqpV89913bo+J9p06dmIkvUmM9nMaLf3X165eRku/2c/IIsDrN7LOZzT0Rl+zehkNfWYfI4OAHuvy2o2M8xltvdDXrV4Gu/8HDhyQH3/8UY4fPy4wIj799NOSK1cuw9Swn+mK+pEfRUvatGklR44cMmHCBOnYsaPe7NMS94E8efLIPffcIzCshkJ07pfq1auHonm2SQJJEjDUAIrWGjRokGSj9gUQ8+OHH35QN51Q3fTs9Qnk+saNG6Vz584u4/q1bt1aBg8eLEWLFk1UBRiKES4AMVcnT55MA2iipP7dUaJECRWKwcgfsCSa5G4SMIQABkLlypVTAyJDKmQlJBBEAvpBXC+D2DSbIgG/CCAsE+LS23vn+FUhDyaBIBHImjWrlC9fXrJlyxakFtkMCRhHoHLlyoKp06FwiPryyy+Vx6J92J53331XpkyZIs2bNzekkzo5tLvKYLh866233BXxal+o7Sqhbt8rWCwcVQT8ToLkL62MGTOqGBGLFy/2t6qwPn7Tpk0qLEBiSU3w1glvhz755BO3/aAxzy0eh52ImYq3bHwId8DCDyYggLenhQsXlkyZMplAW6pIAo4EOOh15MFP5iGQLl069SKa4wbznDNq+i8BjBsKFSqUZA4G8iKBcCSAF/+hePGExEsvv/xyguRLiEnapk0bQULdYAimwCNhEELdOQv2IbnZrVu3nHfZPsPbc9myZbJjxw7RcdhxT0hMkqoTsVgPHTqkDr969arAjmEvaG/58uXKhoMkR0x6aU+H6+FOwHAPUHQ4Li5OeShu2LBBzpw5o97ouAKBLxQC9ELmzJkjjRs3dlXM9Nsw7R2enzr4Md5uPffcc2o6e758+RQD3FiQvfHNN99U0+MnTpzoMnZHKN6Mmf4EsAMkQAIkQAIkQAIkQAIkQAIkQAIk8P8EEJszMYHBEd6ho0aNSqyIYdvHjh0rjz32mOTMmdNWJ+wCn3/+uXqxgWRCMFpC3y5dutjKwPD4zjvvCPa3b99eJT/atm2bwMbi6mV0UnXCUQuer99//708+eSTamYvpuVfunRJJk2aJM8884zMmjVLXn31VRk4cKByMkK4AMxShS0Hjm0UEgh3AoYbQP/55x+pXbu2/P333171vW7dul6VN1NhHfMTOufOnVsWLVokZcuWtXVBG36RqQ3T2+EFimRSv/zyC5Md2ShxhQRIgARIgARIgARIgARIgARIgAT8JwBjoTtJar+7YxPbB2OlNk5i2j+8TEePHq0MoPqYmTNnqqTSv/76qyCpEJyoYF/p2rWrik3atGlTVbRbt27KcWrFihU2x6lVq1apvCzwqrUXT+pEO/DoRCxUOLIhJAyMrkhwjXwlyN0CgyeMozC4QjDbEsmvv/32W3nppZfsm+Q6CYQlAcMNoIhRCeMn3mAgDg1iXhw8eFB9OewJwD0b3p9lypQRfHlxXKQKMrhrwQ3O3vipt2OJqVeIOYI3O3jDUqdOHWUshdGUQgIkQAIkQAIkQAIkQAIkQAIkQAIk4D+BzJkzK2NfYjXFxMQktsvn7TAo6unpMIDu2bNHJUbWFd64cUN5WML7FMZPCGaAtmzZUnl6fvzxxwIDKAypSJyEHCL2Gd9r1KiRIISWp3UiFmunTp1k/vz5cuHCBfnoo4+UrjC8Qo4ePap0ss9ZohM5Y5o+hQTMQMBQAyjcrffu3St//PGH2Ht0IobGBx98IEhKYy/wFoWhr0OHDhEdp/HYsWOq27iJPv744/YIXK4jzudvv/2mAiHXqlVLGUHtbzQuD+JGEiABEiABEiABEiABEiABEiABEiCBJAlg2vmuXbsSLefJc3uiByey46effrIZQFEETmFNmjSxlUZeFDiTjRw5UsaNG2fbjrik9957r+3z8OHD1Xr9+vVt2/QKDLswrmrxtE6URwZ5CDxOtaFWbbD+Q+i+U6dO2Qyu58+ftyVlxhR9CgmYgYChBtAtW7bIE0884WD8BAS8NcCX1DmGRv78+aV69ery/PPPy9SpU83AyycdteEXSU2cbySJVQjXeEyFx7H16tVTBlH7m15ix3H7fwTwQ4F4tAgKz4QG/3HhWvgTwGAIwcfhSc9ESOF/vqihIwEGw3fkwU/mIYBQRHhpjXGDJ1l7zdMzahrpBDBugAECsfgC4bUW6fzYv9ASQAIgxNwMdiIk5N7A1PD9+/cnAFCzZk2VwyPBDoM3wDYAWwmS90K0QRYeoNqG4KpJJDyCvQBem67E3ubgaZ2u6nHeljZtWsXriy++UM8ozZs3V0VwD6KQgBkIGJoFHg89+q2BfefxZmLlypVy9uxZ+81qHbEuf/jhB1m/fn2CfZGyAXExcIPClP87d+541S0kSxo/frxydXfOwOZVRVFYGN7ICD8AIyiFBMxEAMnjEHcIcXgoJGA2Avp3Ti/Npj/1jV4CGKfhoRLxzygkYCYC586dEyQwwfVLIQGzEdi4caOyBeiEwcHSP2vWrMpG0a5dO5tXIxL59OzZUyUnDlbyYUxvr1Chguo2ZtRCkspAj5d1sL1gqnpS4mmdSdWD/YgHipm+SPCM6ficpeoJNZYJJwKGGkDh4Yjp786CNxCIJ4EvN97u2AuMVBBkHItUwdsb3Fjhkbh06VKvu4kkSdOnT5fWrVureB9eVxClB2gvJL2MUgzstgkJ6GtWL03YBapMAmpgTgwkYCYC+p6rl2bSnbpGNwF9zepldNNg781GQF+3ehlM/WNjY5UdApnO4TSDad0jRoxQCYCCqYduq3jx4mr1xx9/1JsclgcOHFBeokg+BFm9erXDfv3B3gPU0zr1sYktMZUeiY5eeeUVqVKlSmLFuJ0EwpqAoQbQPHnyqCC+mLI9aNAgNe39ypUrCgCmuSPIL5IdITsZDKHLly+XgQMHqv2RHjcCQYRhCIUhGBnUvJX7779f5s6dK3/99Ze3h7I8CZAACZAACZAACZAACZAACZAACZCACwJIOIQ8HIEIfxIfH29rMSkjb8OGDdV0eIQHhMHRXuDtiaRHJUuWtMUNHTZsmIojqsth9g3CudjHAPW0Tl1HYsvZs2erXfbT3ZHTBZJUv1Qh/iOBMCBgqAEUbxpg6IMXKLKZ4+3AO++8o7qJDOf4ws6bN08QUwOf4T6tvzT4IkeyIKYJsrWVL19eqlatKjNmzPD6RlGqVCllAKWruWdXio6lopeeHcVSJBB6Arg/Qnjthv5cUAPvCWivA8Ze9p4djwgtAX3P1cvQasPWScBzAhg34N7La9dzZiwZPgRw3WK6eSCMj+HQS2RP16JtH/qz8xLhBHv37q2Mmg8//LB0795dJk2apKblw+uyT58+KrQelrAv/P777/Lss8+qUHtwskJcU3izwlj64Ycfqqn0ntYJXXTyZoSRc5Zy5cqpTfCQXbBggZqhCgMszhsc3RDSUIfhQC4DiK5PfeA/EggHAlYLvuFiTXZksb5FsVh/iC1W922H+j/77DNEyHX4syb6sFi/sA7lIvnD2rVrLdYbm8WaMMqnblpjU1nKlCnj07GhPqhixYrq3Pvad2/0t76JsljfgHlzCMuSQNgQuHHjhgXXMIUEzEbAmrhL3ecvXrxoNtU90he/vxjHWAf5HpUP10LWmGAW66wUi/UBy+HPmlDBYn2oslg9Rhy2P/LII5bvv/8+XLtjiF4cNxiCkZWEiADGDVbvrxC1zmZJwHcC1tifFuvsUK8qwG8UfosXLVrk1XHBLGw1fFpef/11i3WavdIV+lpjfVqsM2AtVq/QRFXBb5HViUzZU7TdxDqN3fLnn386HGM1plpq1aqlbC4oZ3WSslidrCyFChWyWDPLW6whBi2XL19Wx3hSpzVptcXqcKV0hR3HOoPXsm7dOlubqKtp06YWa24TNU7o27eveta25ixR26y5XZRNxxob1FKwYEFVD8aE/fr1s5w8edJWD1d8I9C+fXvF1GoU960CHqUIJMN/6xfGcMGbB/zly5cvQd0LFy6UOXPmyObNmwWZza1fiqBnfUuglMk2wJXejG/JKlWqJEjmZDWAyqxZs0xGneqSAAmQAAkkRSBz5szq999qAI3IbMQYtyBBGbwcrMbQpHCE7X7oX7ZsWa/0Q0xyeH1QSIAESIAESCCUBJBIGVPErQZQwRTvSBSEDIQnZrZs2cRqUFRe3q76CW9P2F0KFCigdiOOKRI8uRJP63R1rN6GpGuoX8/4wXYkWsqQIYMuwmUACHTo0EHFq4VX8OjRowPQQnRUmTJQ3YyJiUn0wQcDaPxRfCdgRuOn773lkSRAAiRAAuFGwDqbQTp27ChWryMH1aweAuozjGtWLwHbPkxv++KLL8TqPWDbxpXQEYAhF9PVTp065aAEQhfhRSWmzuGlpRY86CCED4UESIAESIAESCDwBBCX1P53OLEWs2TJIvjTkpjxE/s9rVPX5WoJg6yz0PjpTISfw5VAwAyg3nQYgXqt06qkS5cu3hwWlWXx8Gh1T1eBj8eMGROVDNhpEiABEiCB0BNAHCvEfEpMXMW52r9/f2LFuT0EBKzT3RO0inheEBg74WFDIQESIAESIAESIAESIIFIIBAWBlAE77UPDhwJYAPRB7i3I7AxXNfHjh0r1jgQYo37EYimWCcJkAAJkAAJuCXQokULQcB9Zw9QZB61xppSyQ7tK8DMBQTsp5AACZAACZAACZAACZAACZBAsAkExACKsKIbN25UWc8RIwLT4axBuV32Dd4giAlqDaLrcj83/kcAYQXw8IisaphKyGzw/7HhGgmQAAmQQPAJILMohQRIgARIgARIgARIgARIgATCnYDhBlAE4G3btq3Mnz8/3PtuOv0QS82a/U2mT58uDRo0kNy5c5uuD8FUOC4uTvbt2yeY4se4JMEkz7b8JQAPOmvWRRXMXAc097dOHk8CwSKwbds2wTVcpUqVYDXJdkjAEALWLLUqrANirmXMmNGQOlkJCQSDAGaHIS4zks9aM0AHo0m2QQKGEUBSPiTRQegV+8Q6hjXAikiABEjg/wkYbgBFHE8aPwN3feXPn19ef/31wDUQQTXDAIoseGfPnqUBNILOazR0BeEucN2mSJHCltExGvrNPkYGgSNHjkh8fLyUL19ezVaIjF6xF9FAQI8bzpw5QwNoNJzwCOrjxYsX1bgBxiMaQCPoxEZJVzBugBEff2nSpImSXrObJEACoSDwX3pWA1rH1HfE84S0a9dOeSseP35cbt++negf3rb36tXLgNZZBQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAk4EjDUAxTxPuG1BK+PyZMne+TCHhsbK/369ZMRI0Y4asZPYUNgzZo18uOPP6qkFv4qpZNdwbsC0yQzZcqU6JtqGMdPnz4tMKzjjXaePHkkW7ZsCVTAfsSS1Yk44DVXrFixBOWw4dq1ayqGKhJ0QALZfurUqVUb9v/YPvkjhq8n1x+umytXrqjviS/XP68/fv/s7z1YD9b9x7ld/TlY7eP+z+vfv+sf5+zw4cNRe/9BOKft27f7PP7g9eff9efv+C8a+V+/fl3davX4Qt939ZL3X44/PR1/mun7p69vLkmABEjAUwKGGkCzZ8+uDFSIuehN/I6sWbMKps5TwpPAhx9+KPPmzTNUuWPHjsnBgwdVnUjshKROzoJ4MEigpQXr1atX1x9tS2zfuXOn7TNWEPMTBlPEk8F1qQVTLGAstZdAtV+wYEH7ZtQ62yd/T66/LFmyqOnveGDR3xNvr39ef/z+Od+AgnX/yZkzp5rC5nxfD1b7uP/z+vfv+se1gxk80Xb/wbgB99pTp06p8YP+DvH+6934i98//75//o5/9XVrv+T9l+NPT8afuGb8vf58+f7jGDyz4eWFp+0nlmDZ/rrnOgmQAAk4E0hm9Z6zOG/05/Orr74qv/32m+zatcuravDDHG3JPmDc+PXXX5VBDp6RMArC6wAGuxw5cijPyAcffFAl8YFXY6gEiYSgpxGXymeffab6Wa9ePRk/fryKsZVYFmEdB1H3G+USS0oAfki6AQErxEp1xQyxZVBW9wX1sX3XWZzJ/984nLz+/iXA7x/vP7z//psU595771Uv3fCQVqZMGfWbEkm/P40bN5ZFixbJxIkTpXbt2uoGwO8/v//8/rtOisXxJ8ffkXT/xw3fLOP/Ro0ayeLFi9XvVcOGDf8drPr5H8+JrjzI/ayWh5OAIQQ6dOggU6ZMke7du8vo0aMNqTMaKzHcAAq3eWTPnDlzpjzwwAMeMcUbn6FDh8r777/vUXmzF0Kc1EmTJsmsWbPUFNek+hMTEyMtW7aUvn37SokSJZIqHtb7cW1s2rRJnnjiCdX/sFaWypEACZAACZCAEwFnA6jTbtN/1AbQhQsXCh4wKSRAAiRAAiQQbgSMNoDCCenJJ58U/Pa5cqIxqv8IVwGjOWZ7aUHSXsyIpZCAOwI0gLqj4/k+Q5MgoVl4CWC6dOfOndX0KXeqwHUdxrCXX37ZFhPPXXmz78OUqhYtWshDDz2kYqQivp8nghvyN998I6VLl5YXX3xRZdf15DiWIQESIAESIAESIAESIAESIAESIAESSJzABx98IEuXLpWxY8cmXsiPPStWrJCKFSuqcIGYXVG2bFn1XP/mm2/KgAED/KiZh5IACXhDwNAYoLphJKrBX+HChV3GdtTlkB0eruYQJEKKZEHSnzp16siePXtUNzHd/7777pPixYur6drp06cX/KVNm1bwZgjxpvAH4yeOWbt2rZo6DnfnAwcOyNy5c+miH8kXDPtGAiRAAiRAAiRAAiRAAiRAAiQQUAJ79+61JWTGjNSnn37aUI/MDRs2CMK/NWvWTDATFN6eFy9eFOTZ+OSTT6RVq1YB7R8rJwES+I+A4QZQuI0/9thjAuMmRBs4/2syOteee+45FeuzR48e0rVrVylXrpzXIJCQ4OOPP5ZRo0bJyJEjBfFWKSRAAiRAAiRAAiRAAiRAAiRAAiRAAt4TwDO1tl2cPXtW+vfvL8OHD/e+okSOeO+999QMTjh86anumTNnliFDhkjy5Mll5cqVCY78+++/VV4VxHv0Rfw93pc2eQwJmIGA4QbQF154wXYDQZyskiVLSqZMmZRnozMQxP6Mi4uT5cuXO++KqM9ICAXD8LJly2wJBXzpILKTjhgxQpo2bSpt2rSRV155Rd00fakrGo5BoqMbN25IunTpoqG77GOEEcC1i0DsGBhRSMBMBOLj41VYGyYSMNNZo64gwHEDrwMzE+C4wcxnL7p1D+W4Ac/o8+fPdzgBX331lUo0g/BzRghC/kEQDs9ZYBStXr26w2Y4kLVu3Vo98zvs8PCDv8d72AyLkYApCRhqAEXWuEOHDilj5w8//KAS3XhCBcmAtmzZ4klRU5ZZs2aNPP/8834ZP+07jqDPyBIP1kUySVOzAABAAElEQVSLFrXfxXU7Art37xZMaahRo4bkzJnTbg9XSSC8CSD0BV6YFCxYUCpUqBDeylI7EnAigJeaCPCPZDo04DvB4cewJoAxA8YO1apVUzHtw1pZKkcCdgQQNgtTa/Pnz6+S0drt4ioJhD0BeEDCMQrjhkAmIHIGAcNr7969nTcrb01sX7BgQYJ9vmxAaMATJ06ohM8Y12fPnt1WDULgYYaoFoyfnnrqKRX+DtPmT58+rRwi4DGqBeHx8FuVMWNGW1xRvc+T41H28OHDAttNmTJlGFZPw+MyKggY6lqEbOX4cuLLiizfnsqjjz7qcCPw9DizlMPbHnjDGikPPPCAHD161MgqI64uxFKF6GXEdZAdilgC+prVy4jtKDsWkQRw3cL7AIkOKSRgJgL6nquXZtKdukY3AXh/QnjtRvd1YNbeX7t2Tc0ghUEymIKwcpip6UrgGYrEzkYIHKEgq1atkipVqgico+wF4fG0fPvttypsHj7Pnj1bJZbGDFAIjMSII/rOO+8o4+cvv/yicq7AmUyLu+NRBv2Cofmjjz6STp06SWxsrHz99df6cC5JIOIJGOoBCk8PeNthyrs3gmlyPXv29OYQU5XNly+f/Pnnn4bpjClamzdvlvLlyxtWJysiARIgARIgARIgARIgARIgARIggUgngATFiPXpThAbFDMv/Q3pAw/PgwcPqrii8LyEvaRbt24qt4e9Zyd0wXa8RIZtpHPnzvLGG2/YVHz77bdlzpw5KkFyiRIl5KGHHlLGUhzTvHlzVc7d8TNnzhRM7//1119VnxD3FBnpYYDNlSuXz1PubQpyhQRMQMBQD1D0F4F6lyxZouJ/edP/RYsWeVPcVGXhrTlx4kTBWxojZMCAAZIqVSrJkiWLEdVFbB166mUwp1JELEx2LKgE9LWrl0FtnI2RgJ8E9HWrl35Wx8NJIGgE9DWrl0FrmA2RgJ8E9FiX166fIHl4SAjo61Yvg6EEEhNhCrg72bdvny07vLtySe1Dv4YNGyY//fSTMjTevXtXxowZI/fdd58KeZXY8cmSJXPYhXrgaGYf2g3hsmDMPXbsmENZfLA/Hl7iMOj26tXLZtCFPaFly5bqOCRappBANBAw3AD6+OOPyyOPPKJiXHgKEF/a1atXe1rcdOUQpxNc8GYGAY2XLl2qYot40xFMJVy8eLFyWYcBFG93KO4J4M0YfljwRotCAmYigBi/5cqVU3F5zKQ3dSUBEMD0rvvvvz+ocbxIngSMIFC8eHEpW7as5M2b14jqWAcJBI0AMktjZpjRIbeC1gE2FNUEKleurMYOMMgFQ7Zt2ybjx4/3qKmBAwe6TF7k0cFOhWBsROxOPMfDOPnPP/9Iw4YNBblTPJEvvvhCTp48acskv2LFCtmwYYM6FJ6l7gR2BGSGHzlypDz22GO2v99++433DXfguC/iCBg6BR7u2kjc0aJFC+VKjanaCMbtTpDIB5nX9NsHd2XNvA9vebZv367e/ODtD9zdMUgpVqyYFCpUSDJkyCAIgoyM5Yh/ghgfiIeCtzmITYIb9blz5xQCxP2Apy3FPQHwLFKkiPtC3EsCYUgAg6LChQuHoWZUiQSSJmDvmZB0aZYggfAhgDEYk0uGz/mgJp4TwLihkPV5gkICZiSAF//BFHhBehqnHIlJ8eztqcE0qX7ABjB27Fhp1aqVtG3bViU5whR5fE6Z0tE0g++1s8BI/M0336jwek2aNFFJz9avXy+wuziL/fE61umXX34pcBKikEC0EnD8lvlJAV9IxJDA2wzI4MGDPa4x0g2guLHDOIwgyHPnzpWLFy+qQMgIhuypYGAO1/UPP/zQ00NYjgRIgARIgARIgARIgARIgARIgASingCci+At7U0uDYSYgGMSnGu8FYQGrF69unJ2sj+2QYMGKiYojKBwcoKdAN6g9mJvwMR2JFZGTFLMsoERFHqtXLnS/hCHdfvj4VwFgQcqDaAOmPghyggYagDFl6xjx44yaNCgKMPoWXdz586tAhcvWLBAJk+erAyhly9fTvJgeNO89NJL8uKLLzrE/EjyQBYgARIgARIgARIgARIgARIgARIgARJQIU4wlTxYAs9LTFV3lXAJs2bxnH/69GlBvNGkDKBPPvmkmsa+du1aj8IM2RtAEeIF8uOPP6rQfM79P3DggEq+VLp0aedd/EwCEUXAUAMoyHTo0EEZQGGwg1t2Uol68CYD2ciiScAFfwhGjLdCuOHFxcXJiRMnBG72MJQWKFBA/SGEQNWqVdXU+GhixL6SAAmQAAmQAAmQAAmQAAmQAAmQgFkJIKzKW2+9peJ+5smTx6Eb8CrVnpnw6tSSNm1atQq7gBYYSeHtmT17dofp+3rmLRIraXF1PIyr8GCdOnWqPPPMMw7GViSDwsxdo6b5az24JIFwJGC4AbRkyZJSp04dlenMOY5FYgBq1aolo0ePTmx3xG7Hzalp06Yu+4d4qjAOp0mThsZPl4S4kQRIgARIgARIgARIgARIgARIgATCkwDyfcDICfvIzJkzVZJTrSkSEsEI2qlTJ5UESm/XsahnzJgh7du3lzVr1qhEyPny5ZOjR49K3759VcxQJDY6cuSIOgxOVcgjUr9+fVssa/vjkaS6d+/eylHt4Ycfli5duki1atVUEiUkQpo3b54gyzyFBCKdQECu8o8++sgjt2wNNzY2VvBFpPxHYNOmTSqBFLK0UXwjgB8UuPN7GuTat1Z4FAkYTwCBzA8ePCiehMgwvnXWSAL+EcDLu+PHj/tXCY8mgRAQuH79uuzfv189RIageTZJAj4TwLgBiWXtPcZ8rowHkkCQCcC7EbE5I1GQ1DRXrlzK4xLxPuH4hSzwMHLC67Jfv34JHMFq1KihZoAigTJmgsLrE/lEhgwZIjCCwnEMU+oRDxSxQLEPCZeR5R3i6visWbMKMtojoRNihyIRU+fOnWXhwoWqjlKlSkUifvaJBBIQMNwDFC3gS+etJJUt3tv6WJ4E9u7dq34I4EWLHwsKCZiFwJkzZ2Tbtm1qwPTAAw+YRW3qSQKKwIYNG1QcKcxwwCCbQgJmIYCQRDAipU6dWoUhMove1JMEkERl69atAqcSJFyhkICZCGzcuFGFhsP1i6TKkSToz/Lly0XH4Dx//rzs2LFDkPkdyYj0dHX7PuPZFV6fcIYoWLCgLTt8mzZtlOfnlStXBNnktcArFDNv9ZgrseMRExTJlN9//32BcTVbtmyqfvtYobpOLkkgUgkExAPUW1jIiD5u3DhvD2N5EnBLQMdC0Uu3hbmTBMKIgL5m9TKMVKMqJJAkAX3d6mWSB7BAWBFACJ5oFX3N6mW0cmC/zUdAX7N6ab4eUONoJqCvW72MNBba+Il+wRMTXqDlypVzafzUfYdREl6iziEFYeS0N36iPAye2viZ1PHYj5d8lSpVkkKFCgmNn5oYl9FCIOQGUEzZgPETSwoJkAAJkAAJkAAJkEBoCGAshhA8kD/++EMt+Y8ESIAESIAESIAESIAEIoGAT1Pg4TaNgfF7772XIINYzZo1PY65ePv2bcFUT8SrQfwLCgkYSQCZ7iB6aWTdrIsEAkkgXbp0qnpeu4GkzLoDRQDX7Y0bNxJ4IwSqPdZrHIHvvvtOMCsH8vXXX8u7774bVb+h+p6rl8aRZU0kEFgCGDfAk4vXbmA5s/bAEMB1ixdwzt6OgWmNtZIACUQzAa8NoIgvgwC6kNdee03Fm9EAs2TJInny5BFkIaOQQKgJlCxZUrn2u4qtEmrd2D4JuCMQExMjTZo0UVNU3JXjPhIIRwK1a9dWDzLMJhqOZydxnRBT7K233rIVQFKKTz/9VCVasG2M8BVMUyxQoIDbaYkRjoDdMymBjBkzqizRkRY/0aSng2p7SQAOVDCAOk/j9rIaFicBEiCBJAl4bQBFJjPEnYCHQIUKFRI00LFjR2UAxZtIGEPdGZ/gAYoBNoIBU0jAaAJ4E+7u+jO6PdZHAkYSQDwfCgmYkQA9OMx41kQGDRokcXFxtjEeegED6HPPPSfRkqiS4wZzXrvU+l8CHDfwSjArAY4bzHrmqDcJmI+A1wbQTJkyqYxkmzdvlrp16ybocbNmzVQGQmRzy5s3b4L9rjYMGTJErl696mpX1G6DB9iDDz4oZcqUiVoG7DgJkAAJkAAJkEDgCSDz+RdffKGm0JYqVUpln8UYBOGO+vTpI9OmTQu8EmyBBEiABEiABEiABEiABAJIwKckSNmyZZP69eu7dFOH4a5Tp04eGz/Rt86dOwewi+asunTp0rJs2TIZNWqUOTtArUmABEiABEiABExBACGNbt68KZjFo7PLwvMTcdmmT58uK1asMEU/qCQJkAAJkAAJkAAJkAAJJEbAJwNoYpXp7T179tSrHi1z5MghLVu29KgsC5EACZAACZAACZAACRhD4Pfff5dZs2YJYgh+9NFHtkpjY2PljTfeUJ979eold+/ete3jCgmQAAmQAAmQAAmQAAmYjYDhBtDDhw9Lzpw5Pebw8ssvqyzwZcuW9fgYFiQBEiABEiABEiABEvCPwJ07d+SVV15Rlbz99tuSO3duhwphAEX8T4Q1mjhxosM+fiABEiABEiABEiABEiABMxEw3ABavXp12bRpk8cMOnToIM2bN5dbt255fAwLkoAnBJDMYfny5Ywv6wkslgkrApiKiimnR44cCSu9qAwJeEJg27Ztsn79ek+KskyICYwbN05wvpDg8tVXX02gDRJaIk475J133lEvrBMUiqANJ0+eVOOGK1euRFCv2JVoIIDnKIwb4IhCIQGzEdixY4esXbtWZYI3m+6Rqi9mffzvf/+T+Pj4SO0i+xWlBAw3gHrLsXLlyrJy5UoZM2aMt4eyPAm4JQAD6Pnz5+Xs2bNuy3EnCYQbgQsXLqjr9tixY+GmGvUhgSQJwHCPa/f27dtJlmWB0BHA7+N7772nFBg6dKgklkH6qaeeklq1agmMgwMHDgydwkFoWY8bzpw5E4TW2AQJGEfg4sWLHDcYh5M1BZkAxg24/0aaQxTGQphJAXtHuXLl1F+9evXUb+n169eDTNmz5mD07NKli+TKlUvg2Hb58mXPDmQpEjAJgZAaQDH4Hjx4sAq8v3TpUpMgo5okQAIkQAIkQAIkYG4C/fv3VwYTJLXETBx3Mnz4cEmePLmMGDFC9u/f764o95EACZAACZAACVgJ5M2bVz799FN5+umn1WwLzLjo27evevmIGRbhKA888IBgfIAQORQSiEQCKf3tFDwDZsyYYasGXxYMplOm/LfqZMmSiau/GzduOExN1llHbRVxhQRIgARIgARIgARIwHACu3btkq+++kpSpEghw4YNS7L+SpUqSefOnWXChAlqqvycOXOSPIYFSIAESIAESCBcCfzyyy+ybt26JNVr3Lix1KlTJ8ly7gqULl3atrtu3bq29XBcgd0mX758UqBAATl37lw4qkidSMAvAn4bQKdPny7wDOjTp49tupu3Lt0ZMmSQZ5991q+O8GAScCaQJ08eZWTPnj278y5+JoGwJpAlSxbJkSOHenMc1opSORJwQaBgwYJqZkeqVKlc7OWmcCDQu3dvFdfrhRdekPvuu88jlQYNGiQ//fSTzJ07VxYtWiSNGjXy6DgzFcK4AdP9cP+lkICZCMCRBNctDBcUEjAbAYwbrl69KqlTpw6a6vPnz5evv/46yfYyZcrktwFUO4ahsbRp0ybZZjgUwKwPCglEIgG/DaCA0qtXL6lSpYo0bdpUELuuYsWKEhMT45YXvlTILFq8eHFp0aKF2L8ZcXsgd5KAhwQQuwR/FBIwGwHE4qtZs6bZ1Ka+JKAIlC1bliTCmMC8efNk4cKFkjVrVq9iet5zzz3y7rvvqnhmMKBu2bLFNtsnjLvrlWqxsbGCPwoJmI0ADEccN5jtrFFfTaBMmTJ6lUsXBBA2cN++fSphYc6cOV2U+G/TiRMnZPfu3YKZG4nZY/bs2aPKZMyYUdltsmXL9l8FXCOBCCdgiAEUjPCji0E1PAIwrapatWoRjo7dIwESIAESIAESIAHzEEBiKp3tHTG+vJ0hgRfeyBy/c+dOGT16tPTs2dM8naemJEACJEACJGAiAogZ+vzzzyunMRiJZ82apWbYjB07NoFX6t69e+Wll15S2/HC8sUXX1QzyfBbXaxYMdVreNm2bdtWMEMHM0AQBuDJJ5+USZMmJRkL3ETYqCoJuCVgmAEUrcAIOnv2bMmdO7fbRrmTBEiABEiABEiABEgguAQQsgheJJh1g4cjbwVeZp9//rk8/vjj0q9fP/Ug5a0R1ds2WZ4ESIAESIAEoo3A2rVrlTETjmU6VCASKCEe6YMPPigzZ86UJ554QmGBofShhx6Sv/76S0qWLKm2wauzVatW8swzz8jKlSvVtrffflsQwxseoCVKlFDHILFht27daACNtgssivtrqAEUHBs0aOARzpMnTwpcuBlfwiNcLEQCJEACJEACJEACPhM4deqUbco7pnm/8cYbLuvCgxFkzJgxsmDBApdlMG0OU/Lef/99GTVqlMsy3EgCJEACJEACJOA9ASSVhucnPDmRgFAL4ofit7ly5crqJSaMnohRCs9PJKbWxk+Ub9asmTJw2oclgt0F5e2n0SP+Kn7rjx07xtwDGjSXEU3AcAOopoUv7s2bN5WLtX0ihMmTJwveXsTFxakvIL6sI0aMkHTp0ulDuSQBEiABEiABEiABEjCQwJIlS+TSpUuqxj///FPw504w1S4pgQcKDaBJUeJ+EiABEiABEvCcwPbt22Xz5s3KsQxZ2e0FsT0xi2PXrl2yatUqFbcanp8wmNoL7C/43beXL774QgYPHmyzu6xYsUI2bNigihw+fJgGUHtYXI9YAoYbQPFGAl9YCNyzP/vsMxVcF5+//PJLefnll7GqBJk2kX3twIED8vvvv+vNXJKAIQQsFovcuHHDdpM3pFJWQgJBIoBrF9NN6SUfJOBsxjAC8fHxcvfu3aBmczVM+QiuCAknEf/rypUrbnsJ7xLEEsPDlL03iauDIi3eO8cNrs4yt5mFAMcNZjlT1NOZAMcNjkRgAIUklpQPRlAYQHfs2CGY3QHJkiWLWib1D4bRb775Rr0EbdKkiUqWtH79esHvH4UEooGA4QbQDBkySK1atWTq1KkObxG2bt0qr732mo0pYk3ApXvNmjWCtxGIHQpXbQoJGEUAGfDwEFejRg0HV3+j6mc9JBAoAvDSWrZsmWBaSoUKFQLVDOslgYAQWL58uZoB0rhxYxrwA0LYt0rTpEkjXbt2TfJgTIXDbycSIyCxZTQJ+o2xAwy7mHpIIQGzEIBTCZxJ8ufPrwwaZtGbepIACCBGJV7QYdyQIkWKqIcCBwgIZsy6kly5cqnNMGYiHA0EsTyTEhhL8bt+//33KyMoWOv4oEkdy/0kECkEDDWA3rp1S31RN23aJIgPZS/du3cXZB+FwPCJ7GUQDDLxJf71119pAFVE+M8oAtevX1dV6aVR9bIeEgg0AX3N6mWg22P9JGAkAVy38OZAKBx6MBtJlnUFmoC+5+ploNtj/SRgFAF4f0J47RpFlPUEk8C1a9eUnQBjh2g2gOL727t3b+nVq5fCDw9PjKWcmeBFHeTee++12Ve+/fZbNdPWeco8yubNm1eFHsSLzb///luQYMm5TlUh/5FAFBAw1ACKL1i9evUSGD9/+eUXWb16tcKJL+DIkSMd0D722GMyYcIEh238QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJGEmjYsGECm4Wr+qtWrepqs1fbEBbIE/nggw/UtPdSpUrJAw88oGbKYlbGo48+ajscU9VhV0GIGsy6heds1qxZBQ5ogwYNknfffddW9vjx4zJu3Dj5/PPP5fTp08rbM3v27Mqoqgv9888/atVTHfVxXJKAWQkYagAFBLzBsRdMyXj99ddtmz755BNJnz697TNWTpw4IZiaRSEBIwlozyO+4TKSKusKBgF97eplMNpkGyRgFAF93eqlUfWyHhIINAF9zeploNtj/SRgFAE91uW1axRR1hNMAvq61ctgtN26dWvBXzDk6NGjtmZglIRDmBYYNBHzE3lRkBgaU9LhxQnDJYygSB5dp04d5cGJY+C9iVBZ06ZNU7HWMV3+008/VSFu3nvvPfnjjz+kdu3aKrkRyiK+JyRbtmySL18+gS6os1WrVrJ48WI5cuSI2o+ESfDArV+/vorjrrcjOzwMrBQSiBQChhpA8SZi/vz5gjcJiEEDl20E0UeSIwi8Q9u1a5eA3YwZM6R48eIJtnMDCfhDoESJEhITE6NCLPhTD48lgWATyJEjh5QrV06wpJCA2QhUqVJFDaL1A7nZ9Ke+0UsAY1HEsrd/OI1eGuy5mQjAQFG+fHll5DCT3tSVBEAASZRv3rwpiGkZSQLj4fDhwwXT07UUK1ZM5aaA4RNJCfEHwyME32MYPSF4DoDxsm3btsqGghCCsK1MmTJFxfuFkVNLly5dlDfrK6+8IkuXLlX7a9asKf/73/+U0RPlMCYbMmSI9OnTR0aPHq0SKA0YMECaN2+u4oIiAWLhwoVl3bp1MmzYMDlz5oyqHlPye/bsKY8//rhujksSMDUBQw2g8OLs0aOHFClSROBWvm3bNvWWAYTwID9+/PgEsPAlfvPNN+W7775LsI8bSMAfAvA0xrVIIQGzEcCbXwxCKCRgRgI5c+Y0o9rUmQQkXbp0UrRoUZIgAdMRwLihUKFCptObCpMACETqC3+8TIN3Jv58EcT43LJli4rbuXPnTvX7BGMkvu/O0qZNG8HfoUOHVEZ4V16b2A/PTxhdM2fObKsCXqEpU6a0xQX9/vvvBX8UEohEAoYaQAEIcSfwxgBvEbTgTccPP/xgG1Riyju8PqdPny5//fWXKmb/JdTHcUkCJEACJEACJEACJEACJEACJEACJEAC0UigYMGCgj9PJCkHCniCOttdGIrQE7IsEykEDDeA4gsEt2rElti8ebOaigFXbvsvFuKC4k3PSy+9pP4AMzY2NlKYsh8kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJhQsBwA6juF6ZhJDYVAzGWGPNTk+KSBEiABEiABEiABEiABEiABEiABEiABEiABEggUASSB6pid/Xevn3b3W7uIwESIAESIAESIAESIAESIAESIAESIAESIAESIAFDCITEAFqxYkWVJAkBgTdt2iTIgkYhAaMJXLt2TQ4cOKAy5hldN+sjgUASwD3x4MGDgnAhFBIwG4FTp07J8ePHzaY29SUBuX79uuzfv9+WkZdISMAsBDBuQPKTS5cumUVl6kkCNgKnT58WZEynkAAJkECgCfhkAP3zzz9lwoQJKtbn8OHDZdSoUV7puWzZMjU9/q233pJKlSqp+J/IBk8hASMJ7N27V7Zv3y5xcXFGVsu6SCDgBJBIbtu2bYKMjxQSMBuBDRs2yLp16/jyyWwnjvrKvn37ZMeOHTTg81owHYFz587J1q1b1fVrOuWpcNQT2Lhxo6xfv144SzTqLwUCIIGAE/ApBujIkSPl559/VsrVqlVLWrRo4ZWiOXPmlPHjx0v9+vWlbdu2Kms83rhTSMBIAnfv3lXV6aWRdbMuEggkAX3N6mUg22LdJGA0AX3dYolsoxQSMAsB+2vXLDpTTxIAAV67vA7MTIDXr5nPHnUnAXMR8MkAqrs4efJkad++vf6oprOPGDHC9tl5Bd6iMTExts1PP/20rFmzRrA9WbJktu1cIQESIAESIAESIAESIAESIAESIAESIAESIAESIAEjCPg0BR4Nly5d2sH4iW3FihWTRx55RMWfmTRpkuDv+++/l7Rp08pTTz0lGTJkQDEHadeuncNnfiABowikT59eVaWXRtXLekgg0ATSpUunmuC1G2jSrD8QBHDdpk6dmt6fgYDLOgNKQN9z9TKgjbFyEjCQAMYNcCbhtWsgVFYVNAK4blOlSiUpU/rlmxU0fdkQCZCAeQn4fJe57777EvQ6U6ZM0qpVK/X33HPPyTfffCPDhg2TF198MUFZvaFy5cqSMWNG/ZFLEjCMQMmSJVWsWRjgKSRgJgLwlG/SpIkyIplJb+pKAiBQu3ZtldwweXKf37ESJAmEhEDx4sWlQIEC6sV9SBRgoyTgIwE8SzVu3FgZkXysgoeRQMgI1KxZU40bGDYnZKeADZNA1BDw+ekkW7ZsbiHhRxiCh3h3ggek2NhYd0W4jwR8IoA34TR++oSOB4UBgTRp0jA0SBicB6rgPQF4cMCTg0ICZiPAcYPZzhj1tSeAcQNfPNkT4bpZCHDcYJYzFb16nj59mslpI+T0++wB6mo6uz0TbdTUS/t9zutZsmRx3sTPJEACJEACJEACJEACJEACJEACJEACJGA6AhcvXlS5Tv744w+V9BkdgLH3wQcfVOEBH3jgAdP1KZoUvnnzpiD594wZM1Temh49eqjzmRiDX3/9VebMmSOrV69WHs0oV7BgQYGH85tvvpnYYdweZAI+G0CT0lN7f3gSywPTNiwWS1JVcj8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJ+E3gzp07smDBAvnzzz/l7Nmzkjt3bmnYsKHUrVvX77ozZ84s77//viDxc4kSJVR9/fv3l379+vldNysIPAHEs+/du7fA+xNGzaQEuXDwhxw3U6dOVWEeV61axXCPSYEL8v6AGUC96Ud8fLw3xVmWBEiABEiABEiABEiABEiABEiABEiABHwisGnTJpXUeefOnQ7HDxo0SHntIZkzPPj8FcSXhnPY7du35d577/Wrur///lt+++036d69u1/18OCkCSAsDv6qV6+edGG7EmXKlFGfcO0w140dmDBZDQsDKG4GFBIgARIgARIgARIgARIgARIgARIgARIIJIHNmzerpI1Xr16VYsWKSYcOHSRfvnyyb98+mTRpkqxcuVJq1Kgha9eulbx58/qtCmbFwuahZ8n6UuGtW7ekdevW0rRpU18O5zE+EvA2trI+x3rpY7M8LEAEfE6ChJgIRgnciikkYDSBuLg4Wb58ueCHjUICZiKA++uKFSvkyJEjZlKbupKAIrBt2zZZv349aZCA6QicPHlSjRuuXLliOt2pcHQTgGEE44bDhw9HNwj23pQEduzYoQyNwQqJd/fuXWXwxDNi27ZtVXIbTFV/9tln5aOPPpI9e/aoOJ3Hjx8PiqclDKMHDx60nbtdu3Y5fMYOPBvA+AmDLPSG/QQxRp0F9wAYd3FPcCWYeXvo0CG1C/XAC9Ze9u/fL+ADwW/ikiVL5Ny5c/ZFEqz72+aJEydk0aJFAt7Xrl1LUL/ecP78edV/d7YjT1jq+rDE7z3sBYjzCU9g3Xf7MlyPLAI+e4DOnj1b8uTJI+nTp3dJRH+JETg2Mes3bnIIFosvDdyLKSRgJAEYQHGjRDyXpJJ2Gdku6yIBfwlcuHBBXbcpUqSQAgUK+FsdjyeBoBKA4R4D7PLlyyf6+x9UhdgYCXhIQI8bzpw5w2lrHjJjsfAgAEMIxrt4nipUqFB4KEUtSMBDAhg3wGCHvzRp0nh4lO/Fli5dKtu3b5fChQvLxIkTE4xVYmJi5Mcff5SiRYvKvHnz5MCBA2rd9xZdH/nXX3/J+PHjZe7cudK8eXPp2rWrdO7cWRlgccTzzz8vY8aMUQd/++23AuMkBHYY6I8kSu+9957atnDhQvn888/V93/NmjXKvvLZZ59Jly5d1P6tW7fKlClTBNP6n3zySWnQoIF07NhRLl26pBL9wPCIPuMFNup+8cUXBfrBXgNbDmw63bp1U3Xpf/60CS9bxFpFYqGHHnpISpYsqdZxbpwNu3ixDhb58+cXTC+fNWuWMgiPHTtW6tSpo9TxhqXWH/W8+uqrMnDgQMEzF2K1Zs+eXSUy4tR1TSnylj4bQP/55x95++23kyTSp0+fJMuwAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEkgA8/iDw/kSiG1eSM2dOefTRR2XatGnKEAhjqNGCF8Uw6sHxATplyZJFJWSCA0/79u0FBr5OnTpJtWrVlPERBuKePXsqI+kbb7xhU2fmzJny1VdfKccy9AdekLVr11YG1Vy5cqkp89gGD0d4WW7YsEE5B8FAiuPQtxw5ctiMqah7wIABytgIg2nfvn2VARLxS5HRHOJvm4iPiXbgpPTSSy+pOuvVqyeVK1dWXph62jk8XmHkhJ7w0IVAH2x78MEHlR5PPPGEeunuKUvUAa9TGDxhDAZrSJEiRRRrGJu1TmoH/0UUgeQR1Rt2hgRIgARIgARIgARIgARIgARIgARIgARcEICBEYKM7+4Es10hSU0Bd1eHu33wNIXxDoK2tAdnxYoV1XR3bMd0dmexnzl748YN5cXYq1cvmzEXHpstW7ZUh3388cdqCcMijKkQGFwx1R8ep5gC36RJE6lSpYqKhYr9Q4cOVcbF2NhYlQVdO70NHjwYu8WINmG8RLzVLVu2qPpQL7ww4bEKYy3kzp07yvB6zz33KKOv2mj9lzZtWuUZC+9UeKpevnxZvGWJmUowFtsbtnXCK1fMddtcmp+Azx6g6Dos9rhx4CL0RRB3AtON8CWikIDRBPBDgmsMruwUEjATAbwBxptYI4Kum6nf1DUyCGAAiVhViYW/iYxesheRSADjBjxI4f5LIQEzEcicObO6bpHEhUICZiOAcQOe2RLzxjS6P9rwuXfvXrdVIxYoRBtC3Rb2cSeSI0Gc7SlIzASBp6I7Wbx4sSAzPKaojxs3zlYUU9qdM87DkAiBd6i9EVUfpHWB96u9vPDCC8ojFNPoIUa1+fjjj8u7776rsqx//fXXyvsTU+K1YCo+jJGYru+sb6VKlaR06dKCeKmrVq2Sxo0bi9bfE5a4V546dcoWcgFG8e+++041jfCMlMgl4LMBFG8ZhgwZ4vcDDr6cbdq0iVzC7FnICMDlH38UEjAbAcQ/0lNMzKY79SWBsmXLEgIJmJIAvF3wRyEBsxGA4YjjBrOdNeqrCSCuYzAFxjIY3iZPnqyWzgY/6ILp4ohxCaMaYlQGW7TBz1ViKL0POsEACPnyyy+lRIkSat3ofzCcwvFNG2ONalPHGQXnqlWryjPPPCOjRo2y5ZiBARSS2O8yjKDQBUm0cE4TE83LmSUMpYir+sUXX0imTJlUHFbU4VwusXq53ZwEfJ4Cj5gNRnh3IIkSYyyY8+Kh1iRAAiRAAiRAAiRAAiRAAiRAAiRgFgKY7g2DGbz+EOcTuU3sBYa3xx57TCV0ROIfVwZS+/LBXtcGPbQLz1nI7t271TJQ/xCTU08RN6rNrFmzym+//Saffvqp8oBFYiTEO8UMYYj2CNafnfumHZ18tUkhrmjdunXV9HqECrCfDu/cFj9HDgGfDKB4C2KkKzis9+XKlYscquwJCZAACZAACZAACZAACZAACZAACZBA2BFAohskvVm3bp3ynGzWrJlyymrUqJFUqFBBDh48qLKsY8ZruIm9AbR48eJKPWRwdyXIYK89Nl3t92QbsrIjPAxsNhCj2ty4caOa2o6k2Qg3AC92ZHyHYRKiZxTBwxPxQJ1FG32dp/o7l3P1GdP44YT3yiuvqPinrspwW2QS8MkA2r17d0Np4K1K8+bNDa2TlZEACZAACZAACZAACZAACZAACZAACZCAPQFM616zZo3KBI645XPmzFGGNxjG4O2IbOvLli2zTce2P9aX9bt376rDXBnyXNXnahq2jm156dIl2yENGzZUOk6dOlXF5rTtsK4g2RESF5UsWdJ+s9frs2bNUsfozPNGtYlM81qQwX3JkiWCeMY4L5BSpUopIzRidS5YsEAXVUvwWb16tepbrVq1HPY5f3DFcvbs2bZ6dHntCazPld7u7VIf7+m59rZ+lvePgE8GUP+a5NEkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEBoCSHgHw+GxY8dk2rRpKv7kzJkz5eTJkzJixAhJly6dIYqdPn1aJYdEZUePHnWoEwmMIM6Z5nWCJuiiRU/RnjFjhopd+f3336tp4r1791ZxKx9++GGBoxqmksOAi6n+8K6EQReCfkJ0bE31wcU/JCTSgmM++OADQSIk1AeB8diINtHHuXPn6qbUErrCwAqBpysSO8Hw27dvX+WFqnZY/61du1ZgCEbsUz1V3huWevYxzjOMq9OnT5dhw4apmK/wRl2/fr2KLYr2Dh06pJrV/NQHN/+0IRVT92kEdQMqVLusFnEKCQSNQMWKFS3Wa93yxBNPBLxN69sXizXJVsDbYQMkEAgC169ft1h/NANRNeskgYASuH37tsXqTRHQNkJZuTVZg/ods07JCqUaAWvbOv1P9c+alCBgbYRrxRw3hOuZoV6eEOC4wRNKLBOOBHwZN1iNZOq3atGiReHYJaWTdeq4xWo8tFgNh0pXPANnz57dYjUeWqxejpY///zTts9q7LNYPSItVq9NizUbucWapVwdYzXSWsaMGaPqu3HjhsWaLEhtz5Ili8UaP1Ntx2/XO++8Y7EaAm3tWKepq/o1nOHDh1usHpVqP9p6/vnnLdbp/3q3WpYvX17tf+211yzWGKmWzp07W1AP6nYWI9qsU6eOxer5abF6llomTJhgsWaFt1inpCd4/rEabC1Wg6WlcuXKFmu2ewv6Ag7Lly+3qeUtS+uUfkvTpk0tVoOrJW/evBargdWCe+hzzz2ntmEsZE36ZLHGBrVYY58qLtZESZZ+/fpZrEZpW7v2K7/++qvFaoC2WA3ntvOAelCHEdK+fXtVL9qg+E4gGQ4NlfGV7UYfAcQO2bRpk1gNoKLd6QNFAfFO8GapRo0aYRe8OlB9Zr2RQQBvNDHtBsHGEYeIQgJmIvD7778rTwckGNBeB2bSPyldEWsK2WERkyrYmWuT0s2b/dYHThk4cKDydLE/zjqAVx4qjzzyiFgfwGy74InRsmVLadCggW1bpK3A6wMxxZCEAR4uFBIwCwHE58O9F9NIdZw+s+hOPUnAarxSyXwwbkiRIoVHQBCrE9PVrQZQm8egRweavBBMN4hPimcEZKi3l1u3binvzmzZsqn9+N32RvDMsWXLFjlz5ozyvkTWd0yfd3dO/GkTx8J7E1Pc0WbhwoXdet3CwxPjL3jCIg6pt/1zxQKet0jGZF8Xkjwh6324SYcOHWTKlCnKy3f06NHhpp5p9HH81phGbSpKAkkTsL7FUYX0MukjWIIEwoOAvmb1Mjy0ohYk4BkBXLfx8fFq2k8kGkA9oxD+pWDAhQE0MYEh1FnwUjGSDaD6nquXzv3nZxIIVwJWzzClGq/dcD1D1MsdAeuMPcFLOYwd3Bnb3NURLftgqNNT4Z37DGOiUS9AYETFX1LiT5s4FhIbG6v+kmoLRl/8GSmu+hiOxk8j+xztddEAGu1XAPtPAiRAAiRAAiQQdQTg6YGZGPC8sJfz588LvCycvc/x0FW/fn37olwnARIgARIgARKIMAKcIBxhJ5TdcSBAA6gDDn6IJALa84hvEiPprEZHX/S1q5fR0Wv2MlII6OtWLyOlX5HYD4SjofxHQF+zevnfHq6RQHgT0GNdXrvhfZ6onWsC+rrVS9eluDWQBJCsRyf7QeIlJIiikEAkEqABNBLPKvukCJQoUUJiYmIkV65cJEICpiKAQQeyE3LwYarTRmX/nwCyhHIaGy8HMxJATDFMfbMmRDCj+tQ5igkghp01gYlHU1ajGBO7HqYErMltVOzwVKlShamGka0WQuL0799fxcLEveTVV18VxGNFpnf72JiRTYG9ixYCNIBGy5mOwn6mT59eihQpEoU9Z5fNTgCDDQQCp5CAGQnkzJnTjGpTZxJQyRcSi61GPCQQzgQwbihUqFA4q0jdSCBRAnzhnyiaoOxAcseffvopKG2xERIINYHkoVaA7ZMACZAACZAACZAACZAACZAACZAACZAACZAACZBAoAjQABoosqyXBEiABEiABEiABEiABEiABEiABEiABEiABEgg5ARoAA35KaACJEACJEACJEACJEACJEACJEACJEACJEACJEACgSJAA2igyLJeEiABEiABEiABEiABEiABEiABEiABEiABEiCBkBOgATTkp4AKBIrAtWvX5MCBA3Lnzp1ANcF6SSAgBCwWixw8eFAuX74ckPpZKQkEksCpU6fk+PHjgWyCdZNAQAhcv35d9u/fL/Hx8QGpn5WSQKAIYNxw6NAhuXTpUqCaYL0kEDACp0+flmPHjgWsfk8qPnLkiAwcOFAuXrzoSXGWIQESMCkBGkBNeuKodtIE9u79v/buBE6Oqk4c+EOOJIQQbghHCPclwUAgHHLIDQKissipuCgEkQXRXW4Ql0sQWVlUxNVF1kVYTjk1IAHlvq9wBQiBhEC4AwkhCc6/f/Xf6p1JZiY9mcmb7pnv+3w63V31qt6rb1Vqun/9jhfS008/nSZNmjT3zHIQqCOBt99+Oz311FPpmWeeqaNaqQqB2gQeeeSR9NBDD/nxqTYuuepIYOzYsWnMmDEC+HV0TlSlNoF33303Pfnkk8X1W9sWchGoH4FHH300Pfzww2nmzJndVqmDDz44nXrqqemYY47ptjoouH4FIkjve1n9np+O1GyhjmSWl0AjCfz9738vqls+N1Ld1bV3C5TXbPncuzUcfaMJlNdtPC+44IKNVn317cUCza/dXszg0BtQwLXbgCdNlasC3X39XnXVVemvf/1rUZ/f/e536cgjj0zDhw+v1m9eX0Rr0p/97GfpzjvvTNG4IdJCCy2Utttuu/S1r30tjRgxYl53bbsMAp988km66KKL0jXXXJMeeOCB9N3vfrc4n20Vfcstt6Qbbrgh3XfffSla5UdaddVV01ZbbZWOOOII10JbcJmXC4BmBlccAQIECBAgQIAAAQIECBAg0L0C06dPT//8z/9cVGLo0KFFS+qjjz463XPPPZ2u2MCBA4tWpfvvv39ae+21i/398Ic/TKeddlqn920H819gkUUWSd/73vdStP6MoObc0u67757iceCBB6bLL788LbbYYunee+8tnmPbaGHsWpib4vxfrwv8/DdWQjcJLLrookXJ5XM3VUOxBDos0K9fv2Ib126H6WxQBwJx3caHRq0/6+BkqEKHBMp7bvncoY1lJtCNAvG5YYEFFkiu3W48CYqeZ4G4bhdeeOGideQ872QeNzzvvPPS+PHj0+c+97miFegKK6xQBK0igNVVaa211iqOL/a3wQYbdGq3UdeLL764U/uwcW0CcU/9zGc+k7bYYovaNvjfXOuvv37xKlp/RhC0eerKa6H5fr2uXUAL0Nqt5GwwgXXWWScNGTIk9e3bt8Fqrrq9XWDxxRdPu+66axFE6u0Wjr/xBLbeeuui6098aJQINJJAfDEZPHiwzw2NdNLUtRCIL9m77LJLNciChUAjCUQX4egynPuH05h46cc//nFBFV3Vo8Xm2Wefnb75zW+m4447Lu29995d9qNCdH2PMU4j0DuvacaMGWnfffdNe+yxx7zuwnbzINDRz7PlOS6fZy+yK66F2ffpfe0Cvp3UbiVngwnErzaCnw120lS3KtCnT5+iNUd1gRcEGkQgPti19aGvQQ5BNXupgM8NvfTE95DDjs8NHf2i3kMO3WE0uEB3fW6IIOfUqVPTP/zDP6RtttmmUPzGN75RjP85YcKEdM4552SRjcDoyy+/XC3r2WefbfE+VsR4lBH8fPDBB4s6R7fs1masf+WVV9Ljjz+eIljaWpo1a1YaN25csSqO/bHHHmuR7cUXX0zlmKxvvvlmuv3221NMstZe6myZb7zxRho1alQxAeG0adPaLOq9994rjj+Ova1Ui2XzbT/66KOi5W+M8xmTHJXH3jyP1z1LQAC0Z51PR0OAAAECBAgQIECAAAECBAi0IRBjOkY392gsE93gyxQ/hEVr0Eg/+clP0quvvlqu6vLnv/3tb+nrX/96Wm655dIZZ5xRjDO57rrrpuhCvcYaa6SRI0dWy4zJmSI4GemPf/xj0Ur1wgsvrK7/85//XLQCjxashxxySLHP//iP/6iuf/LJJ9O//Mu/FJPy/PSnPy32seKKK6aNN944/fznPy8MNt100xQ9ISIAu+2226ZBgwalnXbaKcWwAJdcckl1X+WLzpQZx/P666+nr3zlK8UkQ/HjTUwyFGXOnp566qm05ZZbFh633nprUaf11luvOnFV5O+IZbn/6667Lm244YbFOY6AcYzPueOOO6YIiko9V0AAtOeeW0dGgAABAgQIECBAgAABAgQI/K9AdLePiY7iOSZAirEam6cIth1wwAHp448/rk6Q1Hx9V73eaKON0iqrrJLef//9IpgXs9H/6U9/So8++mgRBP3Vr36V7r///qK4ww47rBoQjS76N910UzrllFOKdddee20RwLzxxhtTbPPQQw+lCKR++9vfLvJFpmgZGS0cI+j4yCOPFMHWCPDG2KcRbP3CF76QItAYKQKlp59+eoqWmREsjXT44Ye3mBiqs2WGeZTTv3//dOSRRxaBxzj+1VZbrUUrzGjxGoHZb33rW+nKK68sJpAKkwEDBqTtttsuXX/99UX9OmIZG8SxRcAzxvc86KCDitcR5B09enSK4KzUcwUEQHvuuXVkBAgQIECAAAECBAgQIECAwP8KRIArgoQrrbRSMdZnazAxNmhMzvQ///M/RevC1vJ0dlmM+R/jjEaK1pgRbIz5K4YNG1Z0d4/l0Z199hStVMsUs9gfe+yxRUA3JqCMFMMQ7bPPPsXrshv/JptsUrQMjYURcI2WohEgjS7wMe/A8OHD05prrllsc/755xfBxWiZGrOgn3jiicXys846q3juijIjeDl27Nj0xBNPpNhfpBgDNgKdEayN9OmnnxaB1+WXX75o8VosrPwTrXZjIqgIYH/nO99JH374YeqoZQwFEF4R/C1TGQhvzbzM47nxBUyCVGfnMMadiJvy3XffnZ5++uniZrjZZpulHXbYIcWzRIAAAQIECBAgQIAAAQIECHRMILo3lwG9CHJGC8TW0sorr1wER0877bQiuPjwww/PlzF2Y/zTSLPPW1EGI6OlYnvptttuK2axv+iii1p0U4+xNGefcT4CiZFissrmQdRy/2Vdll122XJR8XzEEUcULUKjG32kripzr732SieffHLRCjO660eQNrrBlyliIRGMjG7ps9c3uu5HN/jorn/vvfcW3f/L+tdiGed38uTJKcZOjhTji1522WXF6xjTVOq5AgKgGc/tO++8k37zm98Uv3TEWBoxkPGIESOqNYj/bAcffHAR/KwurLyIpt3xnz6a6sevOOV/1OZ5vJ5TYNKkScUvS3EzbeuP25xbWUKg+wVioPP4ZTpmI46HRKCRBKILVVzD0ZpAItBIAjHhw/PPP1+MiRazaksEGkUgxq+LrqLxpT5akEkEGklgzJgxxcQ+0dV59kBXVx/HmWeemeI7YnR9jm7u7aXoHh/f3aOV5G9/+9uidWJ7+btyXekQrRxnT+W6WB4BwEj//u//ntZee+3idVf/E4HT+C5dBmO7qsxovRljd8ZYonHuYwKqGI80Wt5GigBopGiJ2lqKIGjUJa6fXXbZpbUsxbLSa3bLCJTGuKoXXHBB0aX+y1/+cpF/9nxt7tiKhhQQAM102uKXia9+9avVG0cUG//Zjj/++BTNyaOJdwwC3Hwmtgh8bLXVVmmZZZZJL730UopfRmJcijvvvDMtscQSmWreuMXEH7f4NScCzwKgjXsee2PNo2tKXLfRFUQAtDdeAY19zDFhQHQtivGYGn02+PibO2XKlBYnpHx/xx13FD+ylStjAP+YRXbgwIHlIs8NJlB+bnj77beTAGiDnbxeXt2YDTo+N8QXfQHQXn4xNODhx+eGCOLHY3429Bk/fnzx/TuIXnvttZp+qI1xQCOddNJJxTiR9fCdsgzoRb1iJvdIzz333HwLgMb+4zNO2UW8q8pccsklU0xqFGORRkvbSy+9tBifNAKiMRlS2aU//ja3lqJBWaR5/az5i1/8IkVAPCaVih/t42+/1PMFBEAznOO4ccbguuWvJhHUiBnWoqVBjL8R/8Hjy2Lz4GfMPhe/ipRNuaOa8Z8ymuyfeuqpqfmsbxkOQREECBAgQKBXCcREBLvttlubx3zUUUfNsS4mJohWIhIBAgQIECBQXwIR9IweKpEmTJhQPGqtYT01qmkeAI2YQqQYqzS6lM+eohFVBJaju/i8pviBJcbZjLFCI3VVmTHZU7TijJa2++23XxFgvueee1IEJv/1X/81ffazny3Kixae0VgsYijNUwR9I83e1b95nrZeRzf+mHzp3HPPrSkQ3tZ+LG88AQHQDOcsZjQbN25cUVJ0e//1r39dDNQbC6K7SgRHJ06cWK3JCSeckP7pn/6p+r58ES1BY3ayaK4f42HErG0SAQIECBAg0PUC0RoghqUpW3yWJcSXpvgiFB/Mm38JidYRBx54YJnNMwECBAgQIFBHAp///OeLLs8R0Otoim7YMcTEvKaY5yNSBPJqSa11wy7Htmz+uWSnnXYquoxffvnlRRfyeF+m6FEWPU0j9tCZdN111xWbx6ztkbqqzJhpPlpfRlpllVXS7bffnqJV5wMPPFAsi5nsY7jAeB8/Sn/xi18slsc/4XPfffelddZZJ8V5bS+1ZlmW23xdBMgjleeqvX22t67cvq1zPbf17e3bus4LCIB23nCue3jmmWeKPOuvv36Km1PzXy9iYqP4zzt06NAUgxVHnjPOOKPdff7gBz8ommtHYFVqWyBm04sm+ksvvXTbmawhUIcCMcRF/OARs1NKBBpNILpIRQuLee2SVC/HG/8HywHx66VO6jF/BeJzQ7RyiXMvEWgkgRh6I67bzgRoGul41bVnCcTnhvjOVnZ5np9H13zW7/lZTvN9v/XWWy1anjZfF93yI7377rvNF6cXXniheB89RstU1v2aa64pGlBFYHD33XcvZmqPrtzRayVmUd98882LruTRvfymm26qTt5UNrgqx9Ys9zv7cwy7d9xxxxWLY5sf/ehHKSZCKsd2jzFBY3b4zpYZx3jjjTemPffcs1qF+DG5DOLGj8zR+CuCoFGfGGZowIABRd5oRBaB4CuuuKJ63XTEMmIvkaJXbbyOoPgtt9xS9L6NscBj0qt+/foVrUvLhmylX7FhO/+UgdTouj97y9X2roV2dmlVVwpUot7SfBao3Ihi9OKm888/v82SKhMdFXkOOeSQNvOUKypfLJsqLU/Ktw31PGzYsOI4995774aqt8oSIECAAAECBAgQIECAQPcLVIJkxXfKUaNGdX9l2qhBJajWVAkeNlUCh0VdIx5QaZjTVAkeNlWCl0133XVXdV0l2NdUaRHZVGm12VT58bWp8mNGsU3lh42miy++uChh+vTpTZXJgorllcYSTZUAZ7G80qKwqTJGaVMlgFwtp9JNvdh/WbXK8HpNlRaVxfoo6/DDD2+qTLhari6eK2O3F+u///3vN1UmFWqqDOvTFPuJfc+euqLMSkCzqdLys6nSsrSpMtlUU6ULf9MxxxzTVAkatiiuErBtqgQpmyoTGzdVZrtvimMJh7/+9a/VfB21rPzY2bTHHns0VQKuTZUGJ02VAGtTZdjCpkMPPbRYtvPOOzdVhi9sqkxA3VQJ0BculeBrU2Ws0qZKULpabvMXlQBq08iRI5sqgdPqeYj9xD7mdi00309bryu9hov9RhnSvAssEJt2ZUDVvuYUqAQ+U7TavPrqq4uJkObM8f+XxK81X/va14pfVNrKE8vjF6IYNzRajDZvTdreNvWyLsb5iLFOKwHQVDanr5e6qQcBAgQIECBAgAABAgQI1LdAJbCUYhzHSgC02mKwvmvcNbWL0M3LL79cTEjUfK6Q2HuM9RmtO5daaqliffNhemopPYbXe+KJJ4p5R2Lb0/YgdgAALJJJREFUmL8kupi3F2/oTJmxbbT6nTx5clHmaqutVrS6bKuu0cIzetZGS9gYh7Sjx9fafiOuEpMxNd9XtEauh8muZq9vDMv0+9//PlUCoOmXv/zl7Ku9r1FAF/gaoTqTLZprR4rm1O2lo48+ut3/9OW2MUNa3DBeeeWV4gZQLvdMgAABAgQIECBAgAABAgQI9DyBCNSVXeFnP7oIJkZjo65IEUSNx9xSZ8qMbSPF+KrxmFuKoRLi0ZWptWOsx+BnVx5zb9/XZ3o7QI7jrzTRTjH5UYz/GYHLtlLk2XHHHdtaXSz/6KOPUqUZdfE6BjaWCBAgQIAAAQIECBAgQIAAAQKdFdBBuLOCtq9nAQHQTGenMlZF0Xw8Wnm2NSNYNC9fbLHF2q3Rr371qxSD50aKWdIkAgQIECBAgAABAgQIECBAgMC8CER8opzsp/nES/OyL9sQqGcBAdBMZyeClXfffXd677330g477JDK2cE6WnyMyxEpZkMzQ3T7evHrVWUw4/YzWUugTgUqA52nygDjdVo71SLQtsCsWbPa7e3Q9pbWEOheAZ8butdf6Z0T8Lmhc3627j4Bnxu6zz5KHjNmTNpvv/2KsTCji/mxxx6bfvrTnyYtQbv3vCh9/ggYA3T+uLa618rMYemKK64oxu6MAXwrs561mq+9hccff3yqzOCWjjrqqPayWVcReO6559ILL7yQttxyy7TssssyIdAwAlOmTEmjR48uxrmJAcklAo0kUJmVM33yySepMoNoqsyu2UhVV9deLhCfGeKzQ0xKufzyy/dyDYffSAKVGY3THXfcUXy36KoxABvp+NW1sQXuueeeFBPPxOeG9ibcaeyjrN/ab7DBBumqq66q3wqqGYEuFBAA7ULMWnc1ZMiQFI95Seuvv36KhzR3gbL1Z/k89y3kIFAfAuU1Wz7XR63UgkBtAnHdRmuO6E4lAFqbmVz1IVDec8vn+qiVWhCYu0C0/ozk2p27lRz1JzBt2rQ0c+bM4rODAGj9nR81ItCTBDTN6Eln07EQIECAAAECBAgQIECAAAECBAgQINBCQAC0BUf9v7nggguK1qMjR46s/8p2cw3Llkd+SezmE6H4DguU12753OEd2IBANwqU12353I1VUTSBDgmU12z53KGNZSbQjQLlZ13XbjeeBEXPs0B53ZbP87wjGxIgQGAuArrAzwWonla///77KcYAnTFjRorZ4A866KD0+c9/vp6qWFd1WXvttdPiiy+eYgIqiUAjCSyzzDJp6NChKZ4lAo0mMHz4cN3YGu2kqW8hsNZaa6X+/fubZNL10HACSy65ZNpoo43SUkst1XB1V2ECm2yySTF2+MILLwyDAAEC81VAAHS+8nbtziOYFzO/jxs3LsUfiDXWWKNrC+hhe1t00UXT6quv3sOOyuH0BoEFFlggrbbaar3hUB1jDxQw6VwPPKm95JD69evns1UvOdc97TDjc8OQeZxfoKdZOJ7GE/CDf+OdMzUm0KgCAqANdOaiW8Bdd92VrrzyyrTjjjumQYMGNVDtVZUAAQIECBAgQIAAAQIECBAgQIBAfgEB0PzmnSpxlVVWST/4wQ86tQ8bEyBAgAABAgQIECBAgAABAgQIEOgtAiZB6i1n2nESIECAAAECBAgQIECAAAECBAgQ6IUCWoD2wpPe0UN+8skn03XXXZeampo6uukc+SdNmlQse++999Jzzz2XFltssbTyyivPkS8WvP3228WjXBld/gcOHFi+rT5HvV555ZVi8OxYGDNhxjhIrQ2kPX369DR+/PjqsSifv+vP/7/qzaTZC/cf99+4Bsrk74+/vz5/+PxV3g/KZ58/ff72/aN7v3+V/xc9EyBAoFYBAdBapXpxvhNPPDHdfPPNXSowYcKE9Pzzzxf7XH755VsNVj7xxBPpo48+qpYbQdMtttii+r588eGHH6YI0jZPiyyySIqJOCLgGsHQCIpGig8qZbll/vlV/qqrrloWUX1WPv9arr/4UvXQQw+lGTNmVK+djl7/rj///6oXz/++yHX/+eSTT4qZiFdcccUWVchVftz/Xf+u/xYXX+VNLdffxx9/nCZOnFjknTp1anUX7r8d+/zl/1/3/f/79NNP07rrrlu9dssXtVz/Zd7Ofv52/rvv/Dfq37+33nqr+Mwbk/3Wev3FtS4RIECgowICoB0V68L806ZNS7fcckt68cUXUwQE40P3lClT0tJLL51iNrwI3G233XZpk002qQbwurD4mnd19tlnpxEjRqS///3vNW/TVsYbbrghPfroo2nXXXdN66yzTtECtLWWmrH95z73uRR/EMvU1qRPAwYMSBtttFGK1p2RItgZX7zHjBlTtPbs06dPtZVpmMZkUuWxRAvQ+VV+We/mz8rnX8v1Fy3PIvgZ12d8GIzU0eu/+XVXvnb9uf5quf7ieunM/Tf+pr300ktpjz32aPG3y/Xn+stx/c0eeO/I/W/s2LFp3Lhxaa211io+K5Tbuv+2PulmW5+/Srfmz/7/z9////Gd4rXXXkvvvPNOc/bqa/7z1z+gy+8fVfRmL/i37x/fDeN73HLLLVfz54+ycUszZi8JECAwV4EFKi2NmuaaS4YuFbjjjjvSpZdeWnQrb97Csa1CFl988bTPPvuk4447Lq299tptZWuI5d/73vfSv/3bv6ULLrggHXPMMfO1zvHHND4MDhs2LA0ePHi+lmXnBLpS4M0330z3339/8UGwtVbPXVmWfRHoaoHoMTBr1qy0++67t/kDU1eXaX8EukLg8ccfL344jR9VI2AhEWgUgWgwcO+99xYNKLbaaqtGqbZ6EigEbr311uKH/2ggEw1Xakk777xzuu2229KoUaPSTjvtVMsm8hBoaIGDDz44/f73v08jR45Mv/zlLxv6WLqz8gKgGfUnT56cjjjiiHTttdfOU6nRcuLwww9PF154YVpoocZsvFsGQOMXvqWWWmqeHGrdKH5JnDlzZurbt68v4bWiyVcXAhE8iq6Y8f+8X79+dVEnlSBQq0C0/oxruK3W9bXuRz4CuQWiTUA8FlhggeKRu3zlEZhXgejZFF2C4zODH/3nVdF23SUQDYLi3tu/f/8Wre/bq8+rr76aouXzKqusUmzXXl7rCPQEgddff73oLSwA2rmz2ZhRtM4dc7dsHV1at9lmm+r4k/HhZMMNNyy6WcWNe9FFFy0eEayLwEeMaxmP6BIfYwY++OCDRRf5iPbHl8sbb7wxxTgvjZbKFhURDI6HRIAAAQI9U6D5GLY98wgdFQECBOpLIAJCMcmoRKC3CERvP4lAbxLYYIMNetPhdvmxagHa5aSt7/BLX/pSMZFQtAD99re/nYYOHdp6xnaWRtT/nHPOST//+c/Teeedl4499th2ctfvqgjgRstMiQABAgR6nsDw4cNTTCBz1VVXFT/s9bwjdEQECBCoL4GYOCYmLd10003TZZddVl+VUxsC80EgeppE8NNofvMB1y7rViAmeY77vDTvAgKg825X85bPPvtsMQ5ljFOy9dZb17xdWxljrJP99tsvRavS6BYvESBAgACBehEYOHBg0Xvhgw8+SDGGtUSAAAEC81dg9OjRafvtt09f+MIXUsw1IBEgQIAAAQJzCoiezWnS5UseeOCBYuzOrgh+RuVi0OeYJT5mKpUIECBAgAABAgQIECBAgAABAgQIEGhbQAC0bZsuWxNjXXb1WA0jRoxIEyZM6LI62hEBAgQIECBAgAABAgQIECBAgACBniggAJrhrK688srpkUce6bKSYqyTxx9/PG200UZdtk87IkCAAAECBAgQIECAAAECBAgQINATBQRAM5zVaK35n//5n+n666/vktJOP/30tPDCC6cllliiS/ZnJwQIECBAgAABAgQIECBAgAABAgR6qoAAaIYzu8Yaa6S99torffnLX0777rtv+stf/pJi5rqOpE8//TTFJEq77LJLigDoYYcd1pHN5SVAgAABAgQIECBAgAABAgQIECDQKwUW6pVH3Q0HffHFF6enn346XXXVVcUjZsmNcUHXXHPNNGTIkNS/f/+06KKLpn79+hXB0alTp6Zp06aliRMnpphF/qmnnkrvvvtuUfOTTjopjRw5shuOQpEECBAgQIAAAQIECBAgQIAAAQIEGktAADTT+YpZ20ePHl3MBn/jjTemDz74IN17773Fo9YqRHD02GOPTWeccUatm8hHgAABAgQIECBAgAABAgQIECBAoFcL6AKf8fQPGjQo3XDDDenWW29NBxxwQBowYEBNpS+77LLphz/8YRo/frzgZ01iMhEgQIAAAQIECBAgQIAAAQIECBD4/wJagHbDlbDrrrumeEyfPj3dfvvtaezYsWnSpEnpjTfeSFOmTEkRKB08eHDxWGWVVdKmm25adI3vhqoqkgABAgQIECBAgAABAgQIECBAgEBDCwiAduPp69u3b9pjjz1arcGMGTPS5MmTU58+fQQ/WxWykAABAgQIECBAgAABAgQIECBAgMDcBXSBn7tRt+R47LHHUrT+3HPPPbulfIUSIECAAAECBAgQIECAAAECBAgQ6AkCAqA94Sw6BgIECBAgQIAAAQIECBAgQIAAAQIEWhUQAG2VxUICBAgQIECAAAECBAgQIECAAAECBHqCgABoTziLjoEAAQIECBAgQIAAAQIECBAgQIAAgVYFBEBbZbGQAAECBAgQIECAAAECBAgQIECAAIGeICAA2hPOomMgQIAAAQIECBAgQIAAAQIECBAgQKBVAQHQVlksJECAAAECBOZFYPHFF099+vQpHvOyvW0IECBAoGMCcd+NNHDgwI5tKDcBAgQIEOhFAgv1omNtqEONDzLbbbddWn/99Ruq3ipLgAABAr1bYNSoUWnatGkCoL37MnD0BAhkFNhkk03Sbbfdlj772c9mLFVRBAgQIECgsQQWaKqkxqqy2hIgQIAAAQIECBAgQIAAAQIECBAgQKA2AV3ga3OSiwABAgQIECBAgAABAgQIECBAgACBBhQQAG3Ak6bKBAgQIECAAAECBAgQIECAAAECBAjUJiAAWpuTXAQIECBAgAABAgQIECBAgAABAgQINKCAAGgDnjRVJkCAAAECBAgQIECAAAECBAgQIECgNgEB0Nqc5CJAgAABAgQIECBAgAABAgQIECBAoAEFBEAb8KSpMgECBAgQIECAAAECBAgQIECAAAECtQkIgNbmJBcBAgQIECBAgAABAgQIECBAgAABAg0oIADagCdNlQkQIECAAAECBAgQIECAAAECBAgQqE1AALQ2J7kIECBAgAABAgQIECBAgAABAgQIEGhAAQHQBjxpqkyAAAECBAgQIECAAAECBAgQIECAQG0CAqC1OclFgAABAgQIECBAgAABAgQIECBAgEADCgiANuBJU2UCBAgQIECAAAECBAgQIECAAAECBGoTEACtzUkuAgQIECBAgAABAgQIECBAgAABAgQaUEAAtAFPmioTIECAAAECBAgQIECAAAECBAgQIFCbgABobU5yESBAgAABAgQIECBAgAABAgQIECDQgAICoA140lSZAAECBAgQIECAAAECBAgQIECAAIHaBARAa3OSiwABAgQIECBAgAABAgQIECBAgACBBhQQAG3Ak6bKBAgQIECAAAECBAgQIECAAAECBAjUJiAAWpuTXAQIECBAgAABAgQIECBAgAABAgQINKCAAGgDnjRVJkCAAAECBAgQIECAAAECBAgQIECgNgEB0Nqc5CJAgAABAgQIECBAgAABAgQIECBAoAEFBEAb8KSpMgECBAgQIECAAAECBAgQIECAAAECtQkIgNbmJBcBAgQIECBAgAABAgQIECBAgAABAg0oIADagCdNlQkQIECAAAECBAgQIECAAAECBAgQqE1AALQ2J7kIECBAgAABAgQIECBAgAABAgQIEGhAAQHQBjxpqkyAAAECBAgQIECAAAECBAgQIECAQG0CC9WWTS4CBAgQIECgUQXeeuut9Oabb7ZZ/QUWWCANHDgwDRo0KC244IJt5nv//ffThAkT2lxf64oBAwakVVdddY7sf//739Nf/vKX9NRTT6WXX345rb322mn48OFp4403Tn379k0zZsxIP/7xj9Mpp5wyx7YWECBAoF4ExowZk5qamtqszsILL1zcbxdffPE288zv+23zgqOujzzySHHvjb8V06ZNS0OGDElrrrlm8VhxxRWr2S+//PK0+eabp9VXX726zAsCBAgQINAIAgtU/uC1/de5EY5AHQkQIECAAIF2Be677770X//1X+maa65JkydPbjNvBD9XWGGFIvB42GGHpX322ScttND//VZ6ySWXpMMPP7zN7WtdsfPOO6c///nPLbJHwODQQw9NDzzwQIqA7GabbZZmzZqVYnnUYbfddktvvPFGeumll9LEiRNbbOsNAQIE6kng9NNPT3fffXe6/fbb261W/Bi08sorp5122ikdc8wxabXVVqvmn5/327KQDz74IJ111lnF34dJkyYVi/v165c23HDDFOvGjh2b4oeplVZaKW2//fbF+iuvvDLddNNNRZ3L/XgmQIAAAQKNIKALfCOcJXUkQIAAAQKdENhiiy3SL37xi/TYY4+lPn36VPd04IEHpmgd+sorrxRfaM8888w0ePDgNHr06LT//vunNdZYo2iRWW4wffr04mV8af/ud7+brr/++nTvvfemZ599tnjE8jL97ne/K5ZFq6I//elP6YILLigCq7G+3E+ZNwKaW221VRH8PProo9P48ePT/fffnx5++OGi5WoEBm6++eb0t7/9rXj/6aeflpt6JkCAQN0JnHbaaem2224r7pPNK/fQQw+laNn54IMPpt/+9rfpH//xH9O4cePShRdemNZaa6309a9/Pc2cObPYpLxPdvX9tqzPddddV7TuPPfcc1MEP4844oj0xBNPpClTphT34ueeey59+OGHRRA36hA/osUjWuJHC1GJAAECBAg0nEC0AJUIECBAgACB3iFQadkTPT+KRyUoOcdBV758Nx155JHVPJUvvk2PPvpoke+cc85pqrTObKp8sZ9ju1iwxBJLVLe7884758hTaVHUVGlJ1FRp3dli3R577FFsN/vy5plGjRrVVOk2WuSrBEybr/KaAAECdSlQaXVfvSdWhhlptY6VoGhxXyzvywcffHBTpdVl0/y630YlLr744qbPfOYzRd0qQ5803Xrrra3WrVxYCXg2jRw5snosf/jDH8pVngkQIECAQMMIaAHacCFrFSZAgAABAvMusOSSS7a7cXQ3v+iii9Kuu+5a5IsWQAcddFDx+uOPPy66Pe64447t7qOtlTHe3XHHHdeiBejUqVOL1p2xzbBhw9ratCg3WklFev3119vMZwUBAgTqRWBu99uoZ4xzHMOTlClaWV599dVpftxvo4x77rmnaO0ZXdtjLNLKj1XV+31Zh9mfo1t89CIo/y5oATq7kPcECBAg0AgCAqCNcJbUkQABAgQIdJFApdVPdU8x1mZbKcakK9MzzzxTdJWPL+S77757uXienvfaa68WAdCY8Kjys3GxrzvuuKPdfcYYocsuu6wxQNtVspIAgXoRqPV+u+mmm6ZKC/pqte+6664iANrV99sYPuQ73/lO9Z4br2OyuVpS/L349a9/XQyjEn8LJAIECBAg0GgC/zezQaPVXH0JECBAgACB+SYQ44Y2T48//nj61re+lZZZZpnmizv8OmZ/jxZOZWoehI0JN2Kcz6233rpc3eI5WivFGHlagLZg8YYAgQYXiEBpTPxWGeqjOJIYr/nSSy/t8vtttO5/8sknizKWWmqpFGOVdiTFhE0xbqkWoB1Rk5cAAQIE6kVAALRezoR6ECBAgACBOhKIiYiapzXXXLPFDMXN13X0dXzRL9PQoUPTIossUkysEcu+8pWvpD/+8Y9pyy23LLO0eD7llFPSJ5980mKZNwQIEGh0geb33LjfxqRIXZHK+2202jz11FOru9x3331TLV30qxv874vK2KRF69TZl3tPgAABAgTqXeD/+sHVe03VjwABAgQIEMgmELOwlym6na+22mrl2y59jrHlTjjhhOo+33777bTNNtukk08+uUVX+TJDZSKRtNxyy5VvPRMgQKDhBd577730wgsvVI9jxIgR1ddd9SKGMokZ3su0+uqrly879BxjOS+//PId2kZmAgQIECBQDwICoPVwFtSBAAECBAjUkcCrr76arr322mqNDjjggOrr+fHixBNPTLvttlt11zFO3ZlnnpkqM9anyozz1eVeECBAoCcK/OxnP6uOyxljgTa/H3bV8T799NMtdjVkyJAW770hQIAAAQI9XUAX+J5+hh0fAQIECBDogMDzzz+fdt555/Taa68VW+2zzz7p/PPP78AeOp41usDfdNNNRavP6F5ZTor04osvFnU58MADi5npm08S0vFSbEGAAIH6EoiZ2KO1+9lnn11UrH///umWW26ZLy3ux4wZ0+Lga2nVP3Xq1HTrrbe22G72N1/96ldT87GcZ1/vPQECBAgQqBcBAdB6ORPqQYAAAQIEMgtcf/316YMPPkjxJTy6nsdER/fdd1/xPiYrOvzww9P3v//9tOCCC873msUkIGeddVbadddd08iRI9Ozzz5bLfO///u/U8yKHJOC7LDDDtXlXhAgQKBRBGLioBjuY7HFFivuuxMnTky33357mjx5clpooYXSnnvuWayPGeHnR4oft5qnwYMHN3/b6uu4L8+cOTM99NBD6eKLL24x9udOO+2UYhxRiQABAgQINIqAAGijnCn1JECAAAECXSwwadKkIrA4a9asNGDAgKLLeXS93HjjjdMuu+yS4stv7hTjf0Yg9txzz01nnHFGdcKjCRMmFMHRyy67LO2///65q6U8AgQIdEogfmh65JFHioBi7GjQoEHpkEMOSfFj05e+9KW00kordWr/c9t40UUXbZHl3Xffnet4yjFGc9xv4xF/D8reADEOaExWF+slAgQIECDQKAICoI1yptSTAAECBAh0scARRxyRjj766C7ea+d3F13io1todK385je/mR544IFipxGoPeigg9KKK66Ytt12284XZA8ECBDIJBAtP0eNGpWptDmLWWGFFVosjEmX1l133RbL2nuz/vrrV1dH93nBzyqHFwQIECDQIAL5m3Y0CIxqEiBAgAABAt0rsN5666W77747HXXUUdWKRCuq448/vvreCwIECBCYu8CwYcNaZGo+63yLFW28iVafZerbt2/50jMBAgQIEGgYAQHQhjlVKkqAAAECBHqWQIw/GhN+tJdibLwLL7ywxazI999/f/roo4/a28w6AgQIEGgm8MUvfrHFeM6zT4rULGurL5tPdNT8dauZLSRAgAABAnUoIABahydFlQgQIECAQG8QiOBmzPD+4YcfzvVwTzzxxBZ5YkxQiQABAgRqE1h66aXTPvvsU838hz/8Ib300kvV914QIECAAIGeLiAA2tPPsOMjQIAAAQJ1KtC/f/9iHLmLLrporjVsPlZdjBEaY9BJBAgQIFC7QEwuV47d+cknn6Rjjz229o3lJECAAAECDS4gANrgJ1D1CRAgQIBARwTiS2+ZPv300/JllzzPy77XXHPN9KMf/Sg9++yz7dZh/Pjx1fUbbbRR6tOnT/W9FwQIEKhHgXm5J9Z6HPOy78GDB6ff/OY3xYzuUc4NN9yQrr766lqLlI8AAQIECDS0gABoQ58+lSdAgAABAh0TeOedd6obvPXWW9XXnX0xbdq09PHHH1d3U+u+11hjjTR9+vS08847V2d7r+6k2YtLLrmkeLfgggum8847r9kaLwkQIFCfAs3vtzHUR/OgZWdqPK/32yhz//33T3E/jZb0kfbdd9903HHHpZkzZxbv2/pn4sSJba2ynAABAgQINISAAGhDnCaVJECAAAECnRd49NFH09ixY6s7uvnmm1N8ke6KdMUVV7TYzbXXXptixva5pWgBGinG9Nxmm23S+eefn8aNG1fdbOrUqcWs7/GFPSbeOOecc9K2225bXe8FAQIE6lEgWtjP3rryqquu6pKqzuv9tiz80EMPTc8880zae++9U1NTU4qu8VtssUW69NJL05NPPplmzZpVZI3nhx9+OJ1yyinp1FNPLZatuOKK6aSTTip35ZkAAQIECDSMwAKVP3pNDVNbFSVAgAABAgQ6LHDjjTemn/zkJ+m+++6bo5XPkksumTbffPN01FFHtZhpvdZCvvGNb6QnnniieMy+zUorrZQ22WSTFBMYjRgxYvbVxfsrr7wynXDCCenkk09Ojz32WIq6Rnf3ZZddNq2wwgrphRdeKFpNxfYxG/xmm23W6n4sJECAQL0I7LfffsX99tVXX52jSsOGDUtDhw4tgo1zrJzLgs7eb1vb/ejRo9Ppp59e3H+nTJlSZOnbt2+KSZOiJf+MGTPS8ssvn7bffvtiEqW99torxQR2EgECBAgQaDQBAdBGO2PqS4AAAQIEepDAm2++mV577bU0fPjw6lG99957RSukl19+OQ0ZMiTFBEiDBg2qrveCAAECBLpe4PXXX0/PPfdc8cNTjLMcgc+YcG699dbr+sLskQABAgQIZBYQAM0MrjgCBAgQIECAAAECBAgQIECAAAECBPIJGAM0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BARA81kriQABAgQIECBAgAABAgQIECBAgACBzAICoJnBFUeAAAECBAgQIECAAAECBAgQIECAQD4BAdB81koiQIAAAQIECBAgQIAAAQIECBAgQCCzgABoZnDFESBAgAABAgQIECBAgAABAgQIECCQT0AANJ+1kggQIECAAAECBAgQIECAAAECBAgQyCwgAJoZXHEECBAgQIAAAQIECBAgQIAAAQIECOQTEADNZ60kAgQIECBAgAABAgQIECBAgAABAgQyCwiAZgZXHAECBAgQIECAAAECBAgQIECAAAEC+QQEQPNZK4kAAQIECBAgQIAAAQIECBAgQIAAgcwCAqCZwRVHgAABAgQIECBAgAABAgQIECBAgEA+AQHQfNZKIkCAAAECBAgQIECAAAECBAgQIEAgs4AAaGZwxREgQIAAAQIECBAgQIAAAQIECBAgkE9AADSftZIIECBAgAABAgQIECBAgAABAgQIEMgsIACaGVxxBAgQIECAAAECBAgQIECAAAECBAjkExAAzWetJAIECBAgQIAAAQIECBAgQIAAAQIEMgsIgGYGVxwBAgQIECBAgAABAgQIECBAgAABAvkEBEDzWSuJAAECBAgQIECAAAECBAgQIECAAIHMAgKgmcEVR4AAAQIECBAgQIAAAQIECBAgQIBAPgEB0HzWSiJAgAABAgQIECBAgAABAgQIECBAILOAAGhmcMURIECAAAECBAgQIECAAAECBAgQIJBPQAA0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BARA81kriQABAgQIECBAgAABAgQIECBAgACBzAICoJnBFUeAAAECBAgQIECAAAECBAgQIECAQD4BAdB81koiQIAAAQIECBAgQIAAAQIECBAgQCCzgABoZnDFESBAgAABAgQIECBAgAABAgQIECCQT0AANJ+1kggQIECAAAECBAgQIECAAAECBAgQyCwgAJoZXHEECBAgQIAAAQIECBAgQIAAAQIECOQTEADNZ60kAgQIECBAgAABAgQIECBAgAABAgQyCwiAZgZXHAECBAgQIECAAAECBAgQIECAAAEC+QQEQPNZK4kAAQIECBAgQIAAAQIECBAgQIAAgcwCAqCZwRVHgAABAgQIECBAgAABAgQIECBAgEA+AQHQfNZKIkCAAAECBAgQIECAAAECBAgQIEAgs4AAaGZwxREgQIAAAQIECBAgQIAAAQIECBAgkE9AADSftZIIECBAgAABAgQIECBAgAABAgQIEMgsIACaGVxxBAgQIECAAAECBAgQIECAAAECBAjkExAAzWetJAIECBAgQIAAAQIECBAgQIAAAQIEMgsIgGYGVxwBAgQIECBAgAABAgQIECBAgAABAvkEBEDzWSuJAAECBAgQIECAAAECBAgQIECAAIHMAgKgmcEVR4AAAQIECBAgQIAAAQIECBAgQIBAPgEB0HzWSiJAgAABAgQIECBAgAABAgQIECBAILOAAGhmcMURIECAAAECBAgQIECAAAECBAgQIJBPQAA0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BARA81kriQABAgQIECBAgAABAgQIECBAgACBzAICoJnBFUeAAAECBAgQIECAAAECBAgQIECAQD4BAdB81koiQIAAAQIECBAgQIAAAQIECBAgQCCzgABoZnDFESBAgAABAgQIECBAgAABAgQIECCQT0AANJ+1kggQIECAAAECBAgQIECAAAECBAgQyCwgAJoZXHEECBAgQIAAAQIECBAgQIAAAQIECOQTEADNZ60kAgQIECBAgAABAgQIECBAgAABAgQyCwiAZgZXHAECBAgQIECAAAECBAgQIECAAAEC+QQEQPNZK4kAAQIECBAgQIAAAQIECBAgQIAAgcwCAqCZwRVHgAABAgQIECBAgAABAgQIECBAgEA+AQHQfNZKIkCAAAECBAgQIECAAAECBAgQIEAgs4AAaGZwxREgQIAAAQIECBAgQIAAAQIECBAgkE9AADSftZIIECBAgAABAgQIECBAgAABAgQIEMgsIACaGVxxBAgQIECAAAECBAgQIECAAAECBAjkExAAzWetJAIECBAgQIAAAQIECBAgQIAAAQIEMgsIgGYGVxwBAgQIECBAgAABAgQIECBAgAABAvkEBEDzWSuJAAECBAgQIECAAAECBAgQIECAAIHMAgKgmcEVR4AAAQIECBAgQIAAAQIECBAgQIBAPgEB0HzWSiJAgAABAgQIECBAgAABAgQIECBAILOAAGhmcMURIECAAAECBAgQIECAAAECBAgQIJBPQAA0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BARA81kriQABAgQIECBAgAABAgQIECBAgACBzAICoJnBFUeAAAECBAgQIECAAAECBAgQIECAQD4BAdB81koiQIAAAQIECBAgQIAAAQIECBAgQCCzgABoZnDFESBAgAABAgQIECBAgAABAgQIECCQT0AANJ+1kggQIECAAAECBAgQIECAAAECBAgQyCwgAJoZXHEECBAgQIAAAQIECBAgQIAAAQIECOQTEADNZ60kAgQIECBAgAABAgQIECBAgAABAgQyCwiAZgZXHAECBAgQIECAAAECBAgQIECAAAEC+QQEQPNZK4kAAQIECBAgQIAAAQIECBAgQIAAgcwCAqCZwRVHgAABAgQIECBAgAABAgQIECBAgEA+AQHQfNZKIkCAAAECBAgQIECAAAECBAgQIEAgs4AAaGZwxREgQIAAAQIECBAgQIAAAQIECBAgkE9AADSftZIIECBAgAABAgQIECBAgAABAgQIEMgsIACaGVxxBAgQIECAAAECBAgQIECAAAECBAjkExAAzWetJAIECBAgQIAAAQIECBAgQIAAAQIEMgsIgGYGVxwBAgQIECBAgAABAgQIECBAgAABAvkEBEDzWSuJAAECBAgQIECAAAECBAgQIECAAIHMAgKgmcEVR4AAAQIECBAgQIAAAQIECBAgQIBAPgEB0HzWSiJAgAABAgQIECBAgAABAgQIECBAILOAAGhmcMURIECAAAECBAgQIECAAAECBAgQIJBPQAA0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BARA81kriQABAgQIECBAgAABAgQIECBAgACBzAICoJnBFUeAAAECBAgQIECAAAECBAgQIECAQD4BAdB81koiQIAAAQIECBAgQIAAAQIECBAgQCCzgABoZnDFESBAgAABAgQIECBAgAABAgQIECCQT0AANJ+1kggQIECAAAECBAgQIECAAAECBAgQyCwgAJoZXHEECBAgQIAAAQIECBAgQIAAAQIECOQTEADNZ60kAgQIECBAgAABAgQIECBAgAABAgQyCwiAZgZXHAECBAgQIECAAAECBAgQIECAAAEC+QQEQPNZK4kAAQIECBAgQIAAAQIECBAgQIAAgcwCAqCZwRVHgAABAgQIECBAgAABAgQIECBAgEA+AQHQfNZKIkCAAAECBAgQIECAAAECBAgQIEAgs4AAaGZwxREgQIAAAQIECBAgQIAAAQIECBAgkE9AADSftZIIECBAgAABAgQIECBAgAABAgQIEMgsIACaGVxxBAgQIECAAAECBAgQIECAAAECBAjkExAAzWetJAIECBAgQIAAAQIECBAgQIAAAQIEMgsIgGYGVxwBAgQIECBAgAABAgQIECBAgAABAvkEBEDzWSuJAAECBAgQIECAAAECBAgQIECAAIHMAgKgmcEVR4AAAQIECBAgQIAAAQIECBAgQIBAPgEB0HzWSiJAgAABAgQIECBAgAABAgQIECBAILOAAGhmcMURIECAAAECBAgQIECAAAECBAgQIJBPQAA0n7WSCBAgQIAAAQIECBAgQIAAAQIECBDILCAAmhlccQQIECBAgAABAgQIECBAgAABAgQI5BMQAM1nrSQCBAgQIECAAAECBAgQIECAAAECBDILCIBmBlccAQIECBAgQIAAAQIECBAgQIAAAQL5BP4fhqVMj55RsjMAAAAASUVORK5CYII=" width="672" /></p>
<pre class="r"><code># direct
coefplot(m_trst_direct,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code># indirect
coefplot(m_trst_indirect,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code># VOTING

#all
coefplot(m_vote,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   sep = 0.5,
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, 
         col = 1, pch = c(20, 17),
       cex = 0.7,
       legend = c(&#39;2016 Voting&#39;, &#39;2020 Voting&#39;),
       title = &#39;Turnout DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code># direct
coefplot(m_vote_direct,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   sep = 0.5,
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, 
         col = 1, pch = c(20, 17),
       cex = 0.7,
       legend = c(&#39;2016 Voting&#39;, &#39;2020 Voting&#39;),
       title = &#39;Turnout DV&#39;)</code></pre>
<p><img 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width="672" /></p>
<pre class="r"><code># indirect
coefplot(m_vote_indirect,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   sep = 0.5,
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, 
         col = 1, pch = c(20, 17),
       cex = 0.7,
       legend = c(&#39;2016 Voting&#39;, &#39;2020 Voting&#39;),
       title = &#39;Turnout DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#### Type Analysis ####

type_trust1 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Type == 1))</code></pre>
<pre><code>## NOTE: 108 observations removed because of NA values (RHS: 108).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(type_trust1)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.109132   0.090273  1.208907 0.313287    
## PTG         -0.027056   0.064885 -0.416976 0.704737    
## Time        -0.044537   0.035390 -1.258491 0.297243    
## Black        0.079027   0.031859  2.480526 0.089228 .  
## Female      -0.044562   0.011298 -3.944320 0.029053 *  
## Class        0.079984   0.040970  1.952273 0.145949    
## Republican  -0.287090   0.065964 -4.352211 0.022408 *  
## PolInterest  0.019455   0.057259  0.339780 0.756420    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.022768   0.076972 -0.295801 0.786675    
## PTG         -0.030776   0.076429 -0.402678 0.714162    
## Time         0.000948   0.032123  0.029504 0.978316    
## Black        0.065560   0.016017  4.093126 0.026367 *  
## Female      -0.005148   0.050639 -0.101663 0.925438    
## Class        0.074093   0.023480  3.155520 0.051047 .  
## Republican  -0.059136   0.035485 -1.666507 0.194203    
## PolInterest  0.043894   0.054000  0.812858 0.475815    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|) 
## PTS         -0.069441   0.105037 -0.661114  0.55578 
## PTG          0.011602   0.088259  0.131458  0.90373 
## Time        -0.019260   0.039836 -0.483478  0.66185 
## Black       -0.005386   0.042937 -0.125436  0.90811 
## Female      -0.042457   0.019013 -2.233068  0.11168 
## Class        0.094012   0.053037  1.772568  0.17442 
## Republican  -0.062459   0.046375 -1.346819  0.27075 
## PolInterest  0.093857   0.068164  1.376921  0.26230 
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value Pr(&gt;|t|) 
## PTS          0.003919   0.097796  0.040075  0.97055 
## PTG         -0.185844   0.096780 -1.920268  0.15060 
## Time        -0.084259   0.091151 -0.924389  0.42346 
## Black       -0.080564   0.076328 -1.055504  0.36868 
## Female       0.000090   0.101726  0.000887  0.99935 
## Class        0.140389   0.098212  1.429455  0.24822 
## Republican  -0.103191   0.072735 -1.418729  0.25102 
## PolInterest  0.036597   0.101743  0.359702  0.74290 
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.190625   0.081751 -2.331777 0.101982    
## PTG          0.100189   0.059325  1.688803 0.189840    
## Time        -0.055859   0.064281 -0.868970 0.448801    
## Black       -0.134070   0.036722 -3.650920 0.035469 *  
## Female      -0.009264   0.035168 -0.263409 0.809291    
## Class        0.108688   0.048811  2.226699 0.112341    
## Republican  -0.019226   0.049810 -0.385995 0.725247    
## PolInterest  0.039642   0.076449  0.518534 0.639906</code></pre>
<pre class="r"><code>type_trust4 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Type == 4))</code></pre>
<pre><code>## NOTE: 132 observations removed because of NA values (RHS: 132).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(type_trust4)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS          0.023352   0.030562  0.764088 0.5004142    
## PTG          0.072508   0.048814  1.485402 0.2341174    
## Time        -0.021383   0.018843 -1.134826 0.3389183    
## Black        0.050364   0.036585  1.376612 0.2623806    
## Female      -0.023178   0.021438 -1.081189 0.3587740    
## Class        0.020024   0.039197  0.510855 0.6446717    
## Republican  -0.221127   0.026285 -8.412775 0.0035236 ** 
## PolInterest  0.020647   0.015802  1.306575 0.2824971    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.035098   0.023271 -1.508233 0.2286169    
## PTG          0.116898   0.021551  5.424180 0.0122949 *  
## Time        -0.048665   0.033452 -1.454762 0.2417278    
## Black       -0.003562   0.032678 -0.109015 0.9200735    
## Female      -0.068996   0.027158 -2.540545 0.0846392 .  
## Class        0.036031   0.040732  0.884580 0.4415300    
## Republican  -0.084964   0.011677 -7.276279 0.0053576 ** 
## PolInterest -0.002747   0.031462 -0.087320 0.9359195    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.081497   0.012401 -6.571734 0.0071674 ** 
## PTG          0.097544   0.027519  3.544608 0.0382376 *  
## Time        -0.019628   0.024765 -0.792572 0.4859201    
## Black       -0.006919   0.010705 -0.646354 0.5640901    
## Female      -0.025363   0.021679 -1.169932 0.3265184    
## Class        0.037968   0.031024  1.223848 0.3083612    
## Republican  -0.076738   0.024226 -3.167578 0.0505749 .  
## PolInterest  0.100088   0.012118  8.259480 0.0037167 ** 
## ---
## Dep. var.: trstgen
##              Estimate Std. Error  t value  Pr(&gt;|t|)    
## PTS         -0.228174   0.084148 -2.71156 0.0730681 .  
## PTG          0.090359   0.046430  1.94613 0.1468286    
## Time        -0.110885   0.031934 -3.47232 0.0402785 *  
## Black       -0.094053   0.048262 -1.94880 0.1464454    
## Female      -0.105609   0.025637 -4.11945 0.0259252 *  
## Class        0.117233   0.051321  2.28430 0.1065128    
## Republican  -0.137957   0.023180 -5.95141 0.0094873 ** 
## PolInterest  0.179782   0.049164  3.65680 0.0353239 *  
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.252725   0.037700 -6.703643 0.0067732 ** 
## PTG          0.100540   0.050256  2.000546 0.1392523    
## Time        -0.025479   0.024294 -1.048795 0.3713139    
## Black       -0.072609   0.023729 -3.059873 0.0549985 .  
## Female      -0.023582   0.032745 -0.720183 0.5234528    
## Class        0.089710   0.017565  5.107456 0.0145194 *  
## Republican  -0.101292   0.016336 -6.200521 0.0084517 ** 
## PolInterest  0.121024   0.038506  3.142962 0.0515452 .</code></pre>
<pre class="r"><code>type_vote1 &lt;- feols(c(presvote16, presvote20) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Type == 1))</code></pre>
<pre><code>## NOTE: 108 observations removed because of NA values (RHS: 108).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(type_vote1)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: presvote16
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS          0.043847   0.108242  0.405078 0.7125753    
## PTG         -0.017144   0.050003 -0.342862 0.7543208    
## Time         0.103274   0.090846  1.136807 0.3382062    
## Black       -0.034019   0.044010 -0.772997 0.4958426    
## Female      -0.029543   0.042982 -0.687330 0.5412441    
## Class        0.196472   0.018023 10.900927 0.0016523 ** 
## Republican   0.066023   0.008937  7.387934 0.0051283 ** 
## PolInterest  0.261252   0.124841  2.092685 0.1274502    
## ---
## Dep. var.: presvote20
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.029124   0.135743 -0.214554 0.843872    
## PTG          0.058083   0.114961  0.505241 0.648170    
## Time         0.046126   0.068723  0.671179 0.550162    
## Black       -0.047987   0.009802 -4.895399 0.016309 *  
## Female      -0.017673   0.063067 -0.280229 0.797514    
## Class        0.093631   0.042602  2.197816 0.115407    
## Republican   0.005824   0.048026  0.121259 0.911152    
## PolInterest  0.272343   0.094447  2.883568 0.063344 .</code></pre>
<pre class="r"><code>type_vote4 &lt;- feols(c(presvote16, presvote20) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Type == 4))</code></pre>
<pre><code>## NOTE: 132 observations removed because of NA values (RHS: 132).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(type_vote4)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: presvote16
##             Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         0.010882   0.057554  0.189084 0.8620962    
## PTG         0.078821   0.070607  1.116344 0.3456344    
## Time        0.151103   0.045660  3.309302 0.0454159 *  
## Black       0.121837   0.056721  2.148000 0.1209359    
## Female      0.011364   0.060048  0.189243 0.8619817    
## Class       0.110679   0.036831  3.005068 0.0574366 .  
## Republican  0.046491   0.057548  0.807867 0.4782841    
## PolInterest 0.463064   0.043002 10.768499 0.0017127 ** 
## ---
## Dep. var.: presvote20
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.115768   0.047985 -2.412611 0.094795 .  
## PTG          0.039574   0.036485  1.084689 0.357444    
## Time         0.116717   0.031759  3.675089 0.034876 *  
## Black       -0.015449   0.069690 -0.221682 0.838795    
## Female      -0.003223   0.042187 -0.076394 0.943915    
## Class        0.073219   0.036921  1.983120 0.141627    
## Republican   0.004384   0.046858  0.093562 0.931356    
## PolInterest  0.397977   0.034334 11.591348 0.001379 **</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = type_trust1[1],
                           &#39;State&#39;  = type_trust1[2],
                           &#39;Local&#39;  = type_trust1[3],
                           &#39;Interpersonal I&#39;  = type_trust1[4],
                           &#39;Interpersonal II&#39;  = type_trust1[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = type_trust4[1],
                           &#39;State&#39;  = type_trust4[2],
                           &#39;Local&#39;  = type_trust4[3],
                           &#39;Interpersonal I&#39;  = type_trust4[4],
                           &#39;Interpersonal II&#39;  = type_trust4[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;2016 Voting&#39;  = type_vote1[1],
                           &#39;2020 Voting&#39;  = type_vote1[2],
                           &#39;2016 Voting&#39;  = type_vote4[1],
                           &#39;2020 Voting&#39;  = type_vote4[2]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>coefplot(type_trust1,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<pre class="r"><code>coefplot(type_trust4,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>coefplot(type_vote1,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   sep = 0.5,
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, 
         col = 1, pch = c(20, 17),
       cex = 0.7,
       legend = c(&#39;2016 Voting&#39;, &#39;2020 Voting&#39;),
       title = &#39;Turnout DV&#39;)</code></pre>
<p><img 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AgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAHUMECBAgAABAgQIECBAgAABAgQIECBQtwICoHW7a1WMAAECBAgQIECAAAECBAgQIECAAAEBUMcAAQIECBAgQIAAAQIECBAgQIAAAQJ1KyAAWre7VsUIECBAgAABAgQIECBAgAABAgQIEBAAdQwQIECAAAECBAgQIECAAAECBAgQIFC3AgKgdbtrVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQxwABAgQIECBAgAABAgQIECBAgAABAnUrIABat7tWxQgQIECAAAECBAgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAHUMECBAgAABAgQIECBAgAABAgQIECBQtwICoHW7a1WMAAECBAgQIECAAAECBAgQIECAAAEBUMcAAQIECBAgQIAAAQIECBAgQIAAAQJ1KyAAWre7VsUIECBAgAABAgQIECBAgAABAgQIEBAAdQwQIECAAAECBAgQIECAAAECBAgQIFC3AgKgdbtrVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQxwABAgQIECBAgAABAgQIECBAgAABAnUrIABat7tWxQgQIECAAAECBAgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAHUMECBAgAABAgQIECBAgAABAgQIECBQtwICoHW7a1WMAAECBAgQIECAAAECBAgQIECAAAEBUMcAAQIECBAgQIAAAQIECBAgQIAAAQJ1KyAAWre7VsUIECBAgAABAgQIECBAgAABAgQIEBAAdQwQIECAAAECBAgQIECAAAECBAgQIFC3AgKgdbtrVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQxwABAgQIECBAgAABAgQIECBAgAABAnUrIABat7tWxQgQIECAAAECBAgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAHUMECBAgAABAgQIECBAgAABAgQIECBQtwICoHW7a1WMAAECBAgQIECAAAECBAgQIECAAAEBUMcAAQIECBAgQIAAAQIECBAgQIAAAQJ1KyAAWre7VsUIECBAgAABAgQIECBAgAABAgQIEBAAdQwQIECAAAECBAgQIECAAAECBAgQIFC3AgKgdbtrVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQxwABAgQIECBAgAABAgQIECBAgAABAnUrIABat7tWxQgQIECAAAECBAgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAHUMECBAgAABAgQIECBAgAABAgQIECBQtwICoHW7a1WMAAECBAgQIECAAAECBAgQIECAAAEBUMcAAQIECBAgQIAAAQIECBAgQIAAAQJ1KyAAWre7VsUIECBAgAABAgQIECBAgAABAgQIEBAAdQwQIECAAAECBAgQIECAAAECBAgQIFC3AgKgdbtrVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQxwABAgQIECBAgAABAgQIECBAgAABAnUrIABat7tWxQgQIECAAAECBAgQIECAAAECBAgQEAB1DBAgQIAAAQIECBAgQIAAAQIECBAgULcCAqB1u2tVjAABAgQIECBAgAABAgQIECBAgAABAVDHAAECBAgQIECAAAECBAgQIECAAAECdSsgAFq3u1bFCBAgQIAAAQIECBAgQIAAAQIECBDojoBAWwq8/PLLcf3118fq1avbcrO2RYAAAQIECBAgQIAAAQIECBDolAL9+vWLz3zmM7H99tt3yvJ3hEILgHaEvdBAZcjg5z/+4z82UI1VlQABAgQIECBAgAABAgQIECCwbQL77LNPnHrqqdu2kgZeWgC0gXd+e1S9dOXnCSecEIcffnh7FME2CRAgQIAAAQIECBAgQIAAAQKdQuD222+Pp59+2p2027i3BEC3EdDiLRPI4OfFF1/csoUtRYAAAQIECBAgQIAAAQIECBBoAIEMfuZD2jYBgyBtm5+lCRAgQIAAAQIECBAgQIAAAQIECBDowAICoB145ygaAQIECBAgQIAAAQIECBAgQIAAAQLbJiAAum1+liZAgAABAgQIECBAgAABAgQIECBAoAMLCIB24J2jaAQI/FVgzZo18cQTT8ScOXP++qJnBAhsVWD58uXx2GOPxYIFC7aaxwwCBP4qkG0l20y2HYkAgfICeU6W52Z5jiYRIFBeYOrUqZEPiQCB9hEQAG0fd1slQKBKgfxC+vrrr8fcuXOrXFJ2Ao0psHDhwli0aFHMnz+/MQHUmkCVAtlWss1k25EIECgvkOdkeW7mR4PyVnIQSIGXX365eNAgQKB9BARA28fdVgkQIECAAAECBAgQIECAAAECBAgQaAMBAdA2QLYJAgQIECBAgAABAgQIECBAgAABAgTaR6B7+2zWVgkQIFCdwA477BC77rpr8ahuSbkJNKbAwIEDY8CAATFkyJDGBFBrAlUKZFtZtmxZZNuRCBAoLzB06NDo2bNn5DmaRIBAeYHhw4eXzyQHAQKtJiAA2mq0VkyAQC0FunfvHuPHj6/lKq2LQF0L5BfSCRMm1HUdVY5ALQX8yFZLTetqBIERI0ZEPiQCBCoTGD16dGUZ5SJAoFUE3ALfKqxWSoAAAQIECBAgQIAAAQIECBAgQIBARxAQAO0Ie0EZCBAgQIAAAQIECBAgQIAAAQIECBBoFQEB0FZhtVICBAgQIECAAAECBAgQIECAAAECBDqCgABoR9gLykCAAAECBAgQIECAAAECBAgQIECAQKsICIC2CquVEiDQGgKrVq2KtWvXtsaqrZNAXQqsXLky1q9fX5d1UykCtRbItpJtRiJAoDKBPCfLczOJAIHKBFavXh35kAgQaB8BAdD2cbdVAgSqFMgvpZMnT46nn366yiVlJ9CYAgsWLCjazOzZsxsTQK0JVCkwa9asos1k25EIECgvkOdkeW7mh4PyVnIQSIFHH320eNAgQKB9BARA28fdVgkQqFIgrzBYt25drFixosolZSfQmAKlL6TaTGPuf7WuXqDUZkrT6tdgCQKNJZCfL3lu5irQxtrvattygfx88RnTcj9LEthWge7bugLLt57Am2++GYsWLYo+ffrEgAEDWm9D1kyAAAECBAgQIECAAAECBAgQIECgTgVcAdqBd+ztt98eQ4cOjXPPPbcDl1LRCLSNQNeuf3m7Kk3bZqu2QqDzCpTaSmnaeWui5ATaRqDUVkrTttmqrRDovAKltlKadt6aKDmBthHItqK9tI21rRBoSsAVoE2peI0AgQ4n0Ldv3xg9enT069evw5VNgQh0RIEhQ4YUHe3nVCJAoLzAqFGjYvvtt4/dd9+9fGY5CBCI/fffP5YsWRJ5jia1j0AORPXGG29ssfG8zbp3795bvJ7n0T179tzidS+0jcC4cePaZkO2QoBAkwICoE2yeJEAgY4oMHz48I5YLGUi0CEFunfvHiNGjOiQZVMoAh1RIIMF2kxH3DPK1FEF+vfvH/mQ2k/giCOOiMcff7ziAuyyyy7xhz/8IXbYYYeKl5GxdgKDBg2q3cqsiQCBqgUEQKsmq36Bf/7nf45rrrmm6gUXL15cLJOjK77rXe/aYvnf/e53W7zmBQIECBAgQIAAAQIECBCof4G99torZs2atUlF8+rPZcuWRa9evba4OjevdN9uu+02ye8fAgQINIqAAGgb7OkePXrEzJkzW7yl/ADbluVbvGELEiBAgAABAgQIECBAgECHFLj55pu3KNdNN90U5513Xpx99tlx4403bjHfCwQIEGhUAQHQNtjzkyZNKm4zyMGMli9fHn/7t38beWtiuZS3J0ydOjWGDRsWEyZMKJfdfAIECBAgQIAAAQIECBAgQIAAAQIENhMoH4XbbAH/tkzgpJNOikMOOaT4Je7VV1+Nf/3Xfy3bz1T+Ynf++efHQQcdFDkivESAAAECBAgQIECAAAECBAgQIECAQHUCXavLLve2COyxxx7x0EMPxYc//OHIEeC++93vbsvqLEug4QRefPHFKPWN23CVV2ECVQqsWbMm5syZE9kXmESAQHmBbCvZZrLtSAQIlBfIc7I8N5MIEKhMIC+EyodEgED7CAiAtrF7165d47LLLosHH3wwvv71r0deGfqnP/2pjUthcwQ6n8DSpUtj2rRpMX369M5XeCUm0A4C8+bNi+eeey5mz57dDlu3SQKdTyDbSraZbDsSAQLlBfKcLM/N8hxNIkCgvMCUKVMiHxIBAu0jIADaPu5x4IEHxjPPPBMDBw6M/fffv7gytJ2KYrMEOoXAunXrinKWpp2i0ApJoB0FSm2lNG3Hotg0gU4hUGorpWmnKLRCEmhHgVJbKU3bsSg2TaBTCKxfvz7yIREg0D4CAqDt415stU+fPnHDDTfEt771rTjjjDPi7/7u72LVqlXtWCKbJkCAAAECBAgQIECAAAECBAgQIFBfAgKgHWB/Hn/88fHss8/GjBkzir5B8/YriQCBTQV69uwZ2YVE/nAgESBQXqDUVkrT8kvIQaCxBUptpTRtbA21J1BeINtKnpvlOZpEgEB5gd69e0c+JAIE2kfAKPDt477FVgcPHhz3339/XHfddXHooYfGl770pdhuu+22yOcFAo0qkCcLEydOjB49ejQqgXoTqEpg1113jWOOOcYX06rUZG5kgVGjRsWwYcOiV69ejcyg7gQqFhg7dmysXr3a50zFYjI2usCRRx7Z6ATqT6BdBQRA25V/04136dIlLr744jjqqKPi9NNPj9dee23TDP4j0OACrjBo8ANA9asWEMipmswCDSyQ52HaTAMfAKpetYCrP6sms0CDC7iQo8EPANVvdwEB0HbfBVsWIAdFeuqpp+IrX/lKvPDCC8Vt8Vvm8goBAgQIECBAgAABAgQIECBAgAABAuUEBEDLCbXT/LwC4corr2ynrdssAQIECBAgsK0Cv/3tb+Pv//7vtxjgcM6cOcWqR44cuckm8ir3/Ox/97vfvcnr/iFAgAABAgQIECBAYNsEBEC3za/Nl/7GN75R9BN67LHHFiPIt3kBbJAAAQIECBCoSOChhx6Kn/70p1vNWwqEbpzh8MMPFwDdGMRzAgQIECBAgAABAjUQEACtAWJbrWLx4sVxxRVXxNtvvx033nhjnHnmmcWASa29/WuuuSb+6Z/+KdavX7/Nm3rjjTeKddx9992xzz77xA477BDjx48vRpDcfOV55cy8efM2vPzOd74zcoCCzVN2vv7EE0/EW2+9Vczq1q1bZKfs/fv33zxrLFiwIKZNm7ahLrbP3/Gn/WUfZpsn7z/ef2vx+XPRRRfFAQccUFwBumjRoqJbm/wszatCM/3sZz/b5PMvrwA97LDDwvHn+KvF8bfx+5rzH+d/zn+d/5e+y/n+4/uP7z+d+/vPxp/vnlcnIABanVe75u7bt2/svvvuxReo7EB5xIgRbVKemTNnxty5c2u6rVdeeaUIWK5bt25DMHLzDSxdunRDUDPnLV++fPMsxf9r1qyJZcuWxdq1azfMX7Vq1YbnGz9ZuXJl5KOUbL/z+OfxkIHuHNl649tG7X/Hv/bf9Ptfvmc+88wzsffee8egQYOK9z7vf237/t+9e/diYMP8zHnxxRdj++23L338FNO8myN/tNs8+fxrn8//DBLmOU+m0o+q+dz5h/OvPA42T84/1sSSJUs2OY93/u37x+btJP/3/WvT7595MVMpGL25l8//9vn8L+2HzuJfKq9p9QJd/tz4tv2yvuq3a4kWCvzxj3+MH/zgB3H00UfH6NGjW7iW6hbLQ+TVV1+NDBZuazrmmGPiueeei+OOOy5++MMfxnbbbdfkl7/cTm5v4xOp7Bc1R2htKuVVoHkimim/TOZ6t5ZynaW62H7n8c8vo4899lj069cvjjjiiE12r/3v+Nf+t3z/y4BbXvEzfPjwDZ8X3v86xvt/nz59ivewrZ2C+fxrn8//Z599Nl566aXIwSh32223DZ8zzj+cfzn/bPr8+9FHH40MGBxyyCGx0047Of/uIN8/brrppjjvvPPi3HPPLe4azDczn/8d4/N/8uTJxWfL8ccfv+EzZuMnPv/b5/O/tA86sv9ZZ50Vt99+e9x2223FncClMptWJ+AK0Oq82j330KFD49JLL23TcuRJ3+DBg2uyzVJgMq+K6d27d7PrzNtSy+UprSCviM1HJSlvMawk2X7H8t84GL75/rP/Hf/av/e/zd8Xmvrf+7/Pv6aOi81fy/Me5x/Nn6OVzHz+NvbnbykwnO+tpXP80rGx+dT7r/ffzY+Jpv73/atjff9qah+VXvP+377v/6X9YFqdwJYdn1W3vNwECBAgQIAAAQIECBAgQIAAAQIECBDosAKuAO2wu0bBCBDYWCA7bM/+P/MhESBQXmDgwIExYMCAGDJkSPnMchAgULSV7FM4245EgEB5gbwzLa/szHM0iQCB8gLZLZFEgED7CQiAtp+9LRMgUIVAdpuQIxZKBAhUJpBfSCdMmFBZZrkIEPAjm2OAQJUCOSBrWw3KWmXRZCfQIQXaagyPDll5hSLQAQQEQNtxJ6xYsSLuvffemDNnTjHKeo6Mnh2J77zzzsXVB+94xzuKwV7e8573bHWgoHYsvk0TINBgAjnQUHasv2jRok1qPn/+/GKgtDFjxmzyevbj9OEPf7gYhXyTGf4hQIAAAQIECBAgQIAAAQJtKCAA2obYpU098sgj8b3vfS/uvPPOWL58eenlrU779u0bJ510Ulx++eWx1157bTWfGQQIEGhNgXzvuvDCC7e6iZ/85CdbzHv66afjxz/+8Rave4EAAQIECBAgQIAAAQIECLSVgABoW0n/eTuvvfZafOITn4h///d/r2qreVXoLbfcUgRNzzvvvPjmN78ZeTuwRIAAgbYUOOKII+If//EfY/HixZts9uabby6uAP3Yxz4WgwcP3jAvR4c95ZRTNvzvCQECBAgQIECAAAECBAgQaA8BUbQ2Ul+4cGEcdthhMXPmzGKLw4YNi3e/+90xatSoyA7E+/TpUzx69eoVK1eujOyEPx8Z/MxlpkyZEnmL/He+8534/e9/H/fcc09st912bVR6myFAgEAU7zmXXnrpFhT3339/EQC94IILIrvskAgQIECAAAECBAgQIECAQEcSEABto71xzjnnFH19fvKTn4yPf/zjsf/++1e95Xnz5sXXvva1+Na3vhXXX399XHLJJVWvwwIEOrPAqlWriqufu3Xr1pmroewE2kwgf1DLH9byalyJAIHmBdavXx9vvfVW9O7du/mM5hIgUAisXbs2sn/wHAleIkCgvMDq1auLTD169CifWQ4CBGou0LXma7TCLQSef/75eOCBB+LRRx+N//N//k+Lgp+50iFDhhS3v993333x5S9/OdatW7fFtrxAoF4FMpAzefLkyD4lJQIEygssWLCgaDOzZ88un1kOAgRi1qxZRZvJtiMRIFBeIM/J8twsz9EkAgTKC2Q8IB8SAQLtIyAA2gbuv/nNbyL77pwwYUJNtjZx4sRilPgXXnihJuuzEgKdQSCv/syg/4oVKzpDcZWRQLsLlL6QajPtvisUoJMIlNpMadpJiq2YBNpNID9f8twsz9EkAgTKC+Tni8+Y8k5yEGgtgQ4TAJ0/f35r1bHd15uDH+277741LcfBBx8cc+fOrek6rYwAAQIECBAgQIAAAQIECBAgQIBAvQl0iADo8uXL46abbqo32w312WOPPWp62272UfXss8/GAQccsGEbnhCod4GuXf/ydlWa1nt91Y/AtgqU2kppuq3rszyBehcotZXStN7rq34EtlWg1FZK021dn+UJ1LtAthXtpd73svp1ZIEOMQjSHXfcERnUq9eUV2tOmjQp3v/+98cJJ5ywzdX80pe+FNlxcv/+/bd5XVZAoLMI9O3bN0aPHh39+vXrLEVWTgLtKpD9Rmdn+zmVCBAoLzBq1KjYfvvtY/fddy+fWQ4CBIpxDZYsWRJ5jiYRIFBeYNy4ceUzyUGAQKsJVB0AzY6uL7vsspoV6I033ihu5f7CF75Qs3V2tBWNGDEiPvjBD8aHPvShOPnkk4v+QA8//PBiNOtKy5qjLD7yyCNx9dVXF52Nf+c736l0UfkI1I3A8OHD66YuKkKgtQW6d+8e+fkjESBQmUCO/q7NVGYlF4EUyIsxXJDhWCBQucCgQYMqzywnAQI1F6g6AJq3XU+fPr2ur9isufKfV3jDDTfEjBkz4kc/+lHxyKvYsl/QkSNHxjve8Y7iioM+ffpEnnyvWbMm3nzzzWKwl1deeSVyFPnf/va38ac//ako2uc+97k4//zzW6OY1kmAAAECBAgQIECAAAECBAgQIECgrgSqDoDmrxaHHHJI/PrXv46ePXvG4MGDo1u3bi1Cydvec4Cg7AO03tPAgQPj0UcfLa7+vOeeeyJvF3niiSeKR6V1z+DoJZdcEl/+8pcrXUQ+AgQIECBAgAABAgQIECBAgAABAg0tUHUANLWOO+64ePvtt+PJJ5+MLl26bBNgBkEvv/zybVpHZ1k4g8V333133H///XHbbbdFBkKXLVtWtvi77LJLXHjhhXHBBRdEPpcIECBAgAABAgQIECBAgAABAgQIEKhMoEUB0GOPPTbydu1tDX5mEXMdl156aXz729+urMR1kCv98vHWW2/FQw89FLNnz4758+fHq6++GkuXLi2uqh02bFjkY+jQoXHQQQcVt8bXQdVVgQABAgQIECBAgAABAgQIECBAgECbCrQoADpmzJhiZNlalDSDfjvvvHOceuqptVhdp1pHr169iqtpO1WhFZZAOwq8+OKLOtxvR3+b7lwC2Z90tpkc0Tq7UJEIEGheYOXKlZF9r2ff7DmImESAQPMCixcvjnxkm5EIECgvkLGPTLvttlv5zHIQIFBzga4tWWNetXnwwQe3ZNEtlvnVr34VH/nIR2LvvffeYp4XCBAgUBLIq6OnTZtWDMJWes2UAIGtC8ybNy+ee+654i6DrecyhwCBkkDekZNtJtuORIBAeYEcGDfPzfIcTSJAoLzAlClTIh8SAQLtI9CiAGgti7rDDjvET37yk3jwwQdruVrrIkCgzgTWrVtX1Kg0rbPqqQ6BmguU2kppWvMNWCGBOhMotZXStM6qpzoEai5Qaiulac03YIUE6kwgxz/Jh0SAQPsItMr9PdmfZQ7y8/TTT8fChQtj1apVTdbuzTff3HBlSg4OdMwxxzSZz4sECBAgQIAAAQIECBAgQIAAAQIECBBoiUDNA6B//OMfY8KECfHSSy9VVZ7DDz+8qvwyEyDQWAI9e/aMrl27FgOwNVbN1ZZAywRysMJMpWnL1mIpAo0jUGorpWnj1FxNCbRMINvKsmXLIs/RJAIEygvok728kRwEWlOg5gHQE088sQh+7rLLLjFgwICiE/k//OEP8c53vnOTeuSl39nX0n/5L/8lzj333MjlJAIECGxNIE8YJk6cGD169NhaFq8TILCRwK677lrcWeGL6UYonhJoRmDUqFExbNiwyEEqJQIEyguMHTu2GBjX50x5KzkIpMCRRx4JggCBdhSoaQA0b2mfNWtW/L//9/9i4ys6TzvttPiHf/iH2GuvvTapal4t+t//+3+Ps846K7p167bJPP8QIEBgcwEn2JuL+J9A8wICOc37mEtgY4Ec5FOb2VjEcwLNC+SdOc7Nmjcyl8DGAi7k2FjDcwJtL1DTQZByFMATTjhhk+BnVunjH/94XHfddVvUbujQoTF+/Pg477zztpjnBQIECBAgQIAAAQIECBAgQIAAAQIECGyrQE0DoDkC4KBBg7Yo01FHHRW/+tWv4o033thiXt7S+v3vfz+eeuqpLeZ5gQABAgQIECBAgAABAgQIECBAgAABAtsiUNMA6J577lnc/r55gfKWokmTJsVJJ50Ub7/99iaz85b5TLfffvsmr/uHAAECBAgQIECAAAECBAgQIECAAAEC2ypQ0wDokCFDYsmSJUXnvl/5yleK296XL19elDFvc585c2Yx2NETTzxRBEIfe+yxuPLKK4v5L7744rbWxfIECBAgQIAAAQIECBAgQIAAAQIECBDYRKCmgyDllZ5XXXVVfPjDH95wJWiOAJ/9f+YIzl/96lfjox/9aPzsZz+L7DQ7b5kvpb333rv01JQAAQJbCKxZsyamTJkSObL1yJEjt5jvBQIENhXIHyCfeeaZyM/Xprqn2TS3/wgQWLBgQfFj/YEHHhg77LADEAIEygjMmTMnXnvttRg3blx0717Tr5Vltmw2gc4pMHXq1KLgY8aM6ZwVUGoCnVygpleApsWHPvSh+Na3vhXbbbddZED00EMP3UCUt8FfffXVxf8bBz932WWXuOSSSzbk84QAAQKbC2Qw5/XXX4+5c+duPsv/BAg0IbBw4cJYtGhRzJ8/v4m5XiJAYHOBbCvZZrLtSAQIlBfIc7I8Nyvd8Vd+CTkINLbAyy+/HPmQCBBoH4FW+anuggsuiDPPPDOWLl0ae+yxxyY1+7u/+7vYb7/94u67745nn3029t133/jCF77g6pRNlPxDgAABAgQIECBAgAABAgQIECBAgEAtBFolAJoF69u3b/FoqpDHHHNM5EMiQIAAAQIECBAgQIAAAQIECBAgQIBAawrUPAD6wgsvRI4GLxEgQKCWAtkfW/b/mQ+JAIHyAgMHDowBAwZEDlAoESBQXiDbyrJlyyLbjkSAQHmBoUOHRs+ePfWZW55KDgKFwPDhw0kQINCOAjUNgK5du7boA/Spp57SEXY77lSbJlCPAtm5/vjx4+uxaupEoFUE8keDCRMmtMq6rZRAPQr4ka0e96o6tabAiBEjIh8SAQKVCYwePbqyjHIRINAqAjUfBGnatGlFH5833XRTrFixolUKbaUECBAgQIAAAQIECBAgQIAAAQIECBCoRKDmAdDcaL9+/eJLX/pScdvdhRdeGNOnT6+kLPIQIECAAAECBAgQIECAAAECBAgQIECgpgI1D4Cefvrp8Zvf/Cbmzp0bd911VzES/CGHHFLcuvq9730vVq5cWdMKWBkBAgQIECBAgAABAgQIECBAgAABAgS2JlDTAGi3bt3i2muvLbbVpUuXOOKII+K2226LV155Jc4444z4xje+UVwVetFFF8WMGTO2ViavEyBAgAABAgQIECBAgAABAgQIECBAoCYCNQ2AZokGDRq0RcF22mmn+OQnPxnZP+gDDzwQq1atir/5m7+J9773vXHrrbe6KnQLMS8QINCUQL535GBrEgEClQnkXRfr16+vLLNcBBpcINuKO5Ua/CBQ/aoE8pwsz80kAgQqE1i9enXkQyJAoH0Eah4ALVeNIUOGFFeB7rjjjvHEE0/EpEmTYvfddy+uDi23rPkECDSuQH4pnTx5cjz99NONi6DmBKoQWLBgQdFmZs+eXcVSshJoXIFZs2YVbSbbjkSAQHmBPCfLczM/HJS3koNACjz66KPFgwYBAu0j0L0tNrtu3bq477774sYbb4x77713kyu4BgwYEB/72MfixBNPbIui2AYBAp1UIK8wyPeSFStWdNIaKDaBthUofSHVZtrW3dY6r0CpzZSmnbcmSk6gbQTy8yXPzfIcrXfv3m2zUVsh0IkFfL504p2n6HUhUNMAaN4G8ZWvfCX+1//6XwXOvHnz4uabb47/+3//b7z88subgL3nPe+JHCH+tNNO84G5iYx/CBAgQIAAAQIECBAgQIAAAQIECBColUBNA6BZqLzKM38B/NWvfhU///nPY82aNRvK2rNnzzjllFOKwOfBBx+84XVPCBAgUE6ga9e/9NhRmpbLbz6BRhcotZXStNE91J9AOYFSWylNy+U3n0CjC5TaSmna6B7qT6CcgLZSTsh8Aq0rUPMAaF71edlll21S6uHDh8f5558f55xzTuyyyy6bzPMPAQIEKhHo27dvjB49Ovr161dJdnkINLxA9rmdHe3nVCJAoLzAqFGjYvvtty/6pi+fWw4CBPbff/9YsmRJ5DmaRIBAeYFx48aVzyQHAQKtJlDzAGippF26dImjjz66uNrzuOOOi27dupVmmRIgQKBFAvljikSAQGUC3bt3jxEjRlSWWS4CBIo7mLQZBwKBygX69+8f+ZAIEKhMYNCgQZVllIsAgVYRaJUA6MSJE+Oaa66J/fbbr1UKbaUECBAgQIAAAQIECBAgQIAAAQIECBCoRKDmAdCDDjqoGOndFZ+V8MtDgAABAgQIECBAgAABAgQIECBAgEBrCvxlVJEabSE79f2nf/ont7vXyNNqCBAgQIAAAQIECBAgQIAAAQIECBDYNoGaBkCz38/58+fHySefHPfdd9+2lczSBAgQIECAAAECBAgQIECAAAECBAgQ2EaBmgZAly5dGmeeeWb8+Mc/jk9/+tPbWDSLEyBAYFOBF198MRYvXrzpi/4jQKBJgTVr1sScOXNi5cqVTc73IgECmwpkW8k2k21HIkCgvECek+W5mUSAQGUCr776auRDIkCgfQRqGgBdt25d5FWgmY4//vj2qZGtEiBQlwL5A8u0adNi+vTpdVk/lSJQa4F58+bFc889F7Nnz671qq2PQF0KZFvJNpNtRyJAoLxAnpPluVmeo0kECJQXmDJlSuRDIkCgfQRqGgDt379/nHrqqdGrV6/47Gc/W3GN3nzzzbj++usrzi8jAQKNJ5A/sGQqTRtPQI0JVCdQaiulaXVLy02g8QRKbaU0bTwBNSZQnUCprZSm1S0tN4HGE1i/fn3kQyJAoH0EahoAzSpce+21kSPBP/nkkxXXaObMmbFw4cKK88tIgAABAgQIECBAgAABAgQIECBAgACBSgS6V5Kp0jzZZ9KKFSvilltuiQsuuCB+9KMfxbnnnhsDBgxochX5a2H2G/O5z30uPvCBDzSZx4sECBBIgZ49e0bXrl2jT58+QAgQqECg1FZK0woWkYVAQwuU2kpp2tAYKk+gAoFsK8uWLSvO0SrILguBhhfo3bt3wxsAINCeAjUNgC5ZsiRGjhwZb7311oY63XzzzRueN/dEALQ5HfMIEMgThokTJ0aPHj1gECBQgcCuu+4axxxzjC+mFVjJQiAFRo0aFcOGDSu6ciJCgEB5gbFjx8bq1at9zpSnkoNAIXDkkUeSIECgHQVqegv8zjvvHKeccko7VsemCRCoZ4HSVaD1XEd1I1BLgeyTuzQ4YS3Xa10E6lEg20q2GYkAgcoE8s6cPDeTCBCoTCAv5HAxR2VWchFoDYGaXgGaBZw0aVL8y7/8S+y5557Fbe0ZFO3efeubyb4/v//977dG3ayTAAECBAgQIECAAAECBAgQIECAAIEGF9h6ZLKFMIcffngMHz487r333njXu95V0Vre9773VTVoUkUrlYkAAQIECBAgQIAAAQIECBAgQIAAgYYXqOkt8KmZt0J8+tOfjr322qti3PHjx291oKSKVyIjAQIECBAgQIAAAQIECBAgQIAAAQIENhOoeQA01/8//sf/KAKhm21rw79PPvlkLF26dMP/OUr8pz71qQ3/e0KAAAECBAgQIECAAAECBAgQIECAAIFaCLRKADQLliPCf/3rX48zzjgjLrvssk3KunLlyhgzZkxcffXVm7zuHwIECGxNYM2aNfHEE0/EnDlztpbF6wQIbCSwfPnyeOyxx2LBggUbveopAQJbE8i2km0m245EgEB5gTwny3OzPEeTCBAoLzB16tTIh0SAQPsItEoA9OGHH4799tsvrrjiirjjjjti1qxZm9TusMMOi/vuuy9uvPHGIkC6atWqTeb7hwABApsL5BfS119/PebOnbv5LP8TINCEQA4yuGjRopg/f34Tc71EgMDmAtlWss1k25EIECgvkOdkeW7mR4PyVnIQSIGXX365eNAgQKB9BGo+CFI26hNOOKHsB2H2EfqLX/wiRo4cGYMGDYprr722fQRslQABAgQIECBAgAABAgQIECBAgACBuhWo+RWgl1xySRH87NOnT0ycODFOOeWUreINGTKkuAL0uuuuiylTpmw1nxkECBAgQIAAAQIECBAgQIAAAQIECBBoiUDNA6APPfRQHHjggcVtqg888ECcfvrpzZYrb5Vft25dcat8sxnNJECgoQV22GGH2HXXXWPo0KEN7aDyBCoVGDhwYOQgg/ljo0SAQHmBbCvZZrLtSAQIlBfIc7I8N8tzNIkAgfICw4cPj3xIBAi0j0BNb4GfN29eMfjR9ddfHzvttFNFNcq+ljJNnz69ovwyESDQmALdu3eP8ePHN2bl1ZpACwTyC+mECRNasKRFCDSmQAZy8iERIFCZwIgRIyIfEgEClQmMHj26soxyESDQKgI1vQI0Txp79+4d+++/f8WFzZEDMy1evLjiZWQkQIAAAQIECBAgQIAAAQIECBAgQIBAJQI1DYDmFVpjxoyJGTNmVLLtuOuuu+LBBx8s8o4dO7aiZWQiQIAAAQIECBAgQIAAAQIECBAgQIBApQI1DYDmRk866aT44he/GG+99VazZfjpT38akyZN2pDnkEMO2fDcEwIECBAgQIAAAQIECBAgQIAAAQIECNRCoKZ9gGaBLr744vjlL38Zhx12WJx//vnRpUuXopzLli2LmTNnxvPPPx833HBDlG59z5lHH310nH322UU+fwgQIECAAAECBAgQIECAAAECBAgQIFArgZoHQDPgeeutt8bJJ58c55xzTlHOvDW+b9++TZY5R4z/8Y9/HJlHIkCAQHMCq1atKt4runXr1lw28wgQ+E+BlStXRq9evTb8GAmGAIGtC6xfv764gyn7s5cIECgvsHbt2lizZk307NmzfGY5CBCI1atXFwo9evSgQYBAOwjU/Bb4rMOOO+4Y999/f9x3331xwAEHFB+Mm9dt7733jttvvz2efPLJ6Nev3+az/U+AAIFNBDKQM3ny5Hj66ac3ed0/BAg0LbBgwYKizcyePbvpDF4lQGATgVmzZhVtJtuORIBAeYE8J8tzszxHkwgQKC/w6KOPRj4kAgTaR6BVL7s89thjIx95+/vvfve7ePHFF2Pw4MGx1157RY4YLxEgQKBSgbz6c926dbFixYpKF5GPQEMLlL6QajMNfRiofBUCpTZTmlaxqKwEGlIgP1/y3CzP0Vw53ZCHgEpXKeDzpUow2QnUWKDmV4BmkDM/BDdOeUXoQQcdVNwWf+ihh24S/PzUpz4VS5cu3Ti75wQIECBAgAABAgQIECBAgAABAgQIEKiJQM0DoOPHj4+pU6dWXLizzjorPvShD8Xbb79d8TIyEiDQeAJdu/7l7ao0bTwBNSZQnUCprZSm1S0tN4HGEyi1ldK08QTUmEB1AqW2UppWt7TcBBpPINuK9tJ4+12NO45Aq94CX0k13/Oe98SvfvWrYmT4vBpUIkCAQFMCOZDa6NGj9RncFI7XCDQhMGTIkKKz/ZxKBAiUFxg1alRsv/32sfvuu5fPLAcBArH//vvHkiVLtjrYLSICBDYVGDdu3KYv+I8AgTYVqPkVoNWUftGiRfHVr361uGX+4YcfrmZReQkQaECB4cOHR//+/Ruw5qpMoHqB7t27x4gRI/TLVj2dJRpUIPswzDbTrVu3BhVQbQLVCeQ5WZ6bSQQIVCYwaNCgyIdEgED7CGzzFaCnnnpq/OQnP9lQ+rVr18ZRRx0V+cUrU5cuXZp8vPXWW/Hmm29uWM5I8BsoPCFAgAABAgQIECBAgAABAgQIECBAoEYC2xwA/cEPfhDXXXddfOYznylutctyVTu6Wd5u9LGPfaxGVbIaAgQIECBAgAABAgQIECBAgAABAgQI/EVgmwOguZpPf/rTMXbs2DjuuONi8eLFMWbMmLJ9wWTnv0OHDo3sb+nEE0+MffbZxz4hQIAAAQIECBAgQIAAAQIECBAgQIBATQVqEgDNEr33ve+Nn/3sZzFx4sT49re/HYccckhNC2plBAgQIECAAAECBAgQIECAAAECBAgQqFagpoMgZRD0pz/9aQwePLjacshPgACBsgIvvvhicZV52YwyECAQa9asiTlz5lTdLQ06Ao0qkF04ZZvJtiMRIFBeIO/8y3MziQCBygReffXVyIdEgED7CNQ0AJpVOProo6seDXD16tXx0EMPtY+ArRIg0CkEli5dGtOmTYvp06d3ivIqJIH2Fpg3b14899xzMXv27PYuiu0T6BQC2VayzWTbkQgQKC+Q52R5bpbnaBIBAuUFpkyZEvmQCBBoH4GaB0BbUo3JkyfH448/3pJFLUOAQIMIrFu3rqhpadog1VZNAi0WKLWV0rTFK7IggQYRKLWV0rRBqq2aBFosUGorpWmLV2RBAg0isH79+siHRIBA+wjUrA/QjYufH4K//vWv4/nnn4+ZM2c2e/td5sng52c/+9mNV+E5AQIECBAgQIAAAQIECBAgQIAAAQIEtlmg5gHQ119/PU4//XS3tG/zrrECAgQ2FujZs2d07do1+vTps/HLnhMgsBWBUlspTbeSzcsECPynQKmtlKZgCBBoXiDbyrJlyyLP0SQCBMoL9O7du3wmOQgQaDWBmgdAzzzzTMHPVttdVkygcQXyhGHixInRo0ePxkVQcwJVCOy6665xzDHH+GJahZmsjS0watSoGDZsWPTq1auxIdSeQIUCY8eOjRzLQQC0QjDZGl7gyCOPbHgDAATaU6CmAdAcCfCRRx4p6nPqqafGCSecEAcffHDsuOOOTdYxR9nMjuY///nPNznfiwQIENhYwAn2xhqeEygvIJBT3kgOAiWBLl26CH6WMEwJVCCQd+Y4N6sAShYC/yngQg6HAoH2FahpAPTtt9+O7P9z3Lhx8W//9m8V1Wy33XaLq666Ku68886K8stEgAABAgQIECBAgAABAgQIECBAgACBSgVqGgDN2+0y+LnnnntWuv0i3wEHHFD1MlVtQGYCBAgQIECAAAECBAgQIECAAAECBBpSoGuta523vj/44IORt7dXk+bMmVNNdnkJECBAgAABAgQIECBAgAABAgQIECBQVqDmAdCLLroo9ttvv7jlllvKbryUYcaMGXH33XeX/jUlQIAAAQIECBAgQIAAAQIECBAgQIBATQRqegt89gE6e/bsuPLKK+O0004rrgLN2+KbSy+99FLcdtttxYBJzeUzjwCBxhbIq8qnTJkS+Z4ycuTIxsZQewIVCCxfvjyeeeaZ2HvvvWPQoEEVLCELgcYWWLBgQcycOTMOPPDA2GGHHRobQ+0JVCCQd/C99tprRRdo3bvX9GtlBVuXhUDnE5g6dWpR6DFjxnS+wisxgToQqOkn1cqVK4uTxgyEZrrwwgsrJsoR4yUCBAhsTSCDOa+//nrk+4sA6NaUvE7grwILFy6MRYsWxfz58wVA/8riGYGtCmRbyTaTbUcAdKtMZhDYIDB37txYsmRJ5Dla//79N7zuCQECTQu8/PLLxQwB0KZ9vEqgtQVqegt8v3794n3ve19rl9n6CRAgQIAAAQIECBAgQIAAAQIECBAgUJFATa8AzS2eeOKJ8fOf/zxOOumk4naI3r17b7Ug69evL35lv/3227eaxwwCBAgQIECAAAECBAgQIECAAAECBAi0VKDmAdDjjz8+3vnOd8Ydd9wRPXr0qKhcEyZMiMcff7yivDIRINCYAnk7Yvb/Wa5f4cbUUWsCWwoMHDgwBgwYEEOGDNlyplcIENhCINvKsmXLItuORIBAeYGhQ4dGz549dRlRnkoOAoXA8OHDSRAg0I4CNQ+A5pet66+/vuLgZ9b9qKOO0m9MOx4ENk2gMwhk5/rjx4/vDEVVRgIdQiB/NMgfGCUCBCoT8CNbZU5yESgJjBgxIvIhESBQmcDo0aMryygXAQKtIlDTPkBLJXz/+99felrRtGvXrjF27NiK8spEgAABAgTTgTU5AABAAElEQVQIECBAgAABAgQIECBAgACBSgVaFAA9++yzI/vvrFWaM2dO/MM//EOtVmc9BAgQIECAAAECBAgQIECAAAECBAgQKARaFABduXJlvPTSSzUjnDlzZk0DqjUrmBURIECAAAECBAgQIECAAAECBAgQINCpBVoUAM0aX3nllbFixYptrvyiRYvim9/85javxwoIECBAgAABAgQIECBAgAABAgQIECCwuUCLB0G65ZZb4nvf+17ssssu0bt3783XW9H/y5cvjzfeeKO4+vNv/uZvKlpGJgIEGldg1apVkYMhdevWrXER1JxAFQJ5x0avXr2iS5cuVSwlK4HGFMjund56660Wn9c2pppaN7LA2rVrY82aNcVI8I3soO4EKhVYvXp1kbVHjx6VLiIfAQI1FGhxADTLsG7duliwYEENi2NVBAgQaFogAzkPPfRQDBo0KMaNG9d0Jq8SILBBID+f/+M//iP22Wef2GuvvTa87gkBAk0LzJo1K373u9/FIYccUnzWNJ3LqwQIlASefvrp4rvg0Ucf7YeDEoopgWYEHn300WLuxIkTm8llFgECrSXQ4lvgW6tA1tu0QC26G2h6zV4l0DkE8urP/NFFW+gc+0sp218gfzTIpM20/75Qgs4hUGozpWnnKLVSEmg/gfx8yXOzPEeTCBAoL5CfLz5jyjvJQaC1BFp8BWje+n7OOefE8OHDW/yLX94CP3v27Lj55ptbq351s94ZM2bEV77ylfiv//W/xllnnRU77bRT3dRNRQgQIECAAAECBAgQIECAAAECBAi0lkCLA6C33nprvP/9769JuYYNGxbLli2rybo6+kqy34+8JXHatGmxZMmS2HfffWPMmDFFILm5suctv9nv6tixY+Pyyy+Pk08+Of7lX/6luUXMI1BXAl27/uWC9dK0riqnMgRaQaDUVkrTVtiEVRKoK4FSWylN66pyKkOgFQRKbaU0bYVNWCWBuhLQVupqd6pMJxRocQD0sMMOq1l1J0yYEPfee2/N1tdRV/TMM8/ERz/60Zg+ffoWRTzllFPiq1/9aowYMWKLeaUXdt555zjxxBPjmmuuidtuu00AtARj2hACffv2jdGjR0e/fv0aor4qSWBbBYYMGRL5o1tOJQIEyguMGjUqtt9++9h9993LZ5aDAIHYf//9iws68hxNIkCgvIBxDMobyUGgNQVa1Afo2WefXZwg1qpg73rXu+Koo46q1eo65HqmTp1adKrfVPAzC/zDH/6wGKji61//erPl32233ZqdbyaBehbILjf69+9fz1VUNwI1E+jevXvxo1rv3r1rtk4rIlDPAtlW8ofobt261XM11Y1AzQTynCzPzSQCBCoTyMFc8yERINA+Ai26AvS//bf/VtPS7rjjjpFXgdZryitw8srPnGbq0aNH0X9q3s6+xx57FP2gZoD0gQceiCuuuKK4Pf673/1u9OzZcwuSXFYiQIAAAQIECBAgQIAAAQIECBAgQKAygRYFQCtbtVwlgVKfn/n/4MGDY/LkybHffvuVZscxxxxTPM8R4fL29rwKdOLEiXHXXXcZ7GiDkicECBAgQIAAAQIECBAgQIAAAQIEqhdo0S3w1W+msZfIEdxL6Tvf+c4mwc/S6znNW68+//nPF1eE9urVK7Kf1fnz52+cxXMCBAgQIECAAAECBAgQIECAAAECBKoQEACtAqulWV955ZVi0ewg/IMf/GDZ1WQ/n/fdd1/87d/+bRx66KHx+9//vuwyMhAgQIAAAQIECBAgQIAAAQIECBAgsKWAAOiWJjV/Za+99irWueeee0aXLl0qWn/Xrl2LW+H/5//8n3HkkUfGc889V9FyMhGoZ4EXX3wxFi9eXM9VVDcCNRNYs2ZNzJkzJ7J7FYkAgfIC2VayzWTbkQgQKC+Q52R5biYRIFCZwKuvvhr5kAgQaB8BAdA2cD/kkEMiA5qzZ8+OtWvXVrXFc845J/75n/85jjvuuMiBkiQCjSqwdOnSYoCw6dOnNyqBehOoSmDevHnFj2f52SMRIFBeINtK/uCcbUciQKC8QJ6TTZs2LfIcTSJAoLzAlClTIh8SAQLtIyAA2gbueQXoGWecEStWrIiHH3646i3mIEk/+MEP4pRTTonf/OY3VS9vAQL1ILBu3bqiGqVpPdRJHQi0pkCprZSmrbkt6yZQDwKltlKa1kOd1IFAawqU2kpp2prbsm4C9SCwfv36yIdEgED7CAiAtpH7VVddFRkInTRpUosuex83blzcc8898ctf/rKNSmwzBAgQIECAAAECBAgQIECAAAECBDq/QLsEQJ988smGu1Vi9913L67ePOCAA+Kggw6Kn/zkJ1Htr6Xvete7igDoiBEjOv+RpwYEqhTo2bNn0ZVEnz59qlxSdgKNKVBqK6VpYyqoNYHKBUptpTStfEk5CTSmQLaV7OYrz9EkAgTKC/Tu3TvyIREg0D4C3Vtrs0uWLIkbbrghsm+YDP797//9vzdsKjuZHzNmTHziE5+ISy+9dMPr9f6kf//+xejuGQD+/ve/H7fffnvceeedVVX7He94RxEEPfroo6taTmYCnV0gTxYmTpwYPXr06OxVUX4CbSKw6667Rnah4otpm3DbSB0IjBo1KoYNGxa9evWqg9qoAoHWFxg7dmysXr3a50zrU9tCnQjk4MYSAQLtJ9AqAdDs5zJv9Z47d25Rs+OPP36TGh522GFFIPADH/hAMbDPLbfc0lAfnHkFaD5amgYPHlx0ON7S5S1HoLMKCOR01j2n3O0lIJDTXvK22xkFunTpIvjZGXecMrebgKs/243ehjupgAs5OumOU+y6Eah5APTll1+OE044IZYvX94sUvaH+Ytf/CJGjhwZgwYNimuvvbbZ/GZuKtC9e8133aYb8B8BAgQIECBAgAABAgQIECBAgACBOhCoeR+gl1xySRH8zD5h8nbVHLl8a2nIkCHF6OjXXXddTJkyZWvZvL6RwDe+8Y3I2+DPP//8jV71lAABAgQIECBAgAABAgQIECBAgACBpgRqfhnhQw89FAceeGDkdKeddoqf/vSn8d3vfrepbRev7bfffsVgQHfccUfkSOfS1gUWL14cV1xxRbz99ttx4403xplnnhmHHnro1heo0Zwrr7wyrrnmmqoHbWpq82+++Wbx8uzZs+PnP/957LDDDjFhwoSiA/XN80+dOjXmzZu34eUc/CkHgto8pcdjjz0Wq1atKmZ169atOJYGDBiwedaYP39+0e3C+vXri3m2z9/xt+3tb+nSpUV7evzxx+P111/X/v78Web9x/vv5h9Am3/+5PwcDDFvId08+fzz+e/8x/lf6X3B+a/zf99/fP8rvR+Upr7/Nvb3/9JxYFq9QE0DoHmyloMfXX/99UXws5LiLFq0qMiWgyVJzQv07du3GFDqhRdeKAaCaavR4DOgkfu1lmnNmjWRj7feeitKwcjN158BzcxTSqUAZ+n/0jS/QG6ct7Tu0vyNp9lRez5Kyfb5O/7+8mNAqU2Uphu3qXytufZXMsy2t/lypfXlVPvz/uP996+fP9kmSm0nn2+cNm9HzbW/jfP6/PvLucXGlqXn3n+8/3j/+ev7j/Nf578+f7b9/Nfn71+/qzv/aNvzj9K5jWn1Al3+/ObXdOuvfl1FsCqDdBkw23777Ys1lK4Aveuuu5pcY94m/+CDDxajwj/zzDNN5vHiXwX++Mc/xg9+8IPIUeBHjx791xmt/Cz7dK3FoZJXrGaw+4Mf/GD8+Mc/juzLNAcd2Foqnaxmnub6Pc0g6Nq1a4vV5NU0eRXo1lLmy/yZbL/z+Of+yq4ycmTr7Dt442T/t+/x/573vCfy/fs//uM/IkeE1f46xvtPvm/nftl7772LvrazzXj/6xjv/9ttt13xFtbc56rPv78Ea9ry83/BggUxc+bM4k6m0qB7bbn9jT/X7P+23//8/ypQ6fGXd3S99tprRZvJ9zWf/x3j8/+mm26K8847L84999zirsHcsz7/O8bn/29/+9viu++YMWP+2uA2e1Zp+/P9p32//7S1/1lnnRW333573HbbbcWdwJsdNv6tUKCmV4BmMCkb84wZM+Lggw8uW4QMimbwM1N+aZbKCwwdOjQuvfTS8hlrnCNvFa9FKp0YZZCyklHwKsmT5cr1NXUbYVNlzjKUytHU/I1fs/0eG3Ns9Xlb+GcXEPnjSt7ysXkAtC22v9XK/3lGo2+/9CNGfgaUa1vaX9u9/yxcuDDyLou89ToHG8zEv+38C/DN/vDv2P7ZVrLNZNvJ/tYrSY3+/q/+jX3++corrxR3ieWVcL179262yXj/69jvfxvvPN9/Wu/7T17MlKm5ACj/1vPf+Djf2vPO4r+18nu9eYEtO55qPn/ZuSeddFJ88YtfLG5tbi5zXhk6adKkDVkOOeSQDc89IUCAAAECBAgQIECAAAECBAgQIECAQC0EanoFaBbo4osvjl/+8pdx2GGHFSOVl64MWrZsWXFb0fPPPx833HBDPPHEExvKn7dzn3322Rv+94QAAQIECBAgQIAAAQIECBAgQIAAAQK1EKh5ADQDnrfeemucfPLJcc455xRlzNsis2/QplKOGF/qC7Kp+V4jQIBACmQ3DNn/Zz4kAgTKCwwcOLAYjX7IkCHlM8tBgEBkW8kf7LPtSAQIlBfIrrmyv9xadZVVfotyEOjcAsOHD+/cFVB6Ap1coOYB0PTYcccd4/777y8eV1xxRUybNm0LphyU4e///u/jIx/5SMV9N26xkk7+wooVK+Lee++NOXPmxNy5cyP70Vm6dGnsvPPOxcl39j91xBFHRA4wkv3mSAQaWSB/SBk/fnwjE6g7gaoE8gvphAkTqlpGZgKNLOBHtkbe++reEoERI0ZEPiQCBCoTaMtBjCsrkVwEGkugVQKgJcJjjz028pG/pv/ud7+LF198MQYPHhx77bVXQ1/F9cgjj8T3vve9uPPOOyNH6S2X8urZ7Fv18ssvL+zK5TefAAECBAgQIECAAAECBAgQIECAAIG/CLRqALSEnFeEHnTQQcUjX3vrrbdKsxpq+tprr8UnPvGJ+Pd///eq6p1Xhd5yyy1F0PS8886Lb37zm5FXw0kECBAgQIAAAQIECBAgQIAAAQIECDQv0CpRtLyl+xvf+EbRH8xXv/rVLW7ffuqpp+LGG2+Mq666KvbYY4/mS1gncxcuXFgMDDVz5syiRsOGDYt3v/vdMWrUqMj+c/r06VM8evXqFStXriyums0rZzP4mctMmTKluEX+O9/5Tvz+97+Pe+65J7bbbrs60VENAgQIECBAgAABAgQIECBAgAABAq0jUPMA6EsvvRSHH354zJs3ryjx+973vshR3jdOhx56aDEoUt4e/6lPfSrOPffcjWfX5fMcECoDw5/85Cfj4x//eOy///5V1zNNv/a1r8W3vvWtuP766+OSSy6peh0WIECAAAECBAgQIECAAAECBAgQINBIAjUPgH7hC1/YEPzMUTT32WefJj0zAHjDDTcUV0Xusssu8aEPfajJfPXw4vPPPx8PPPBAPProo9s0IEWOTpq3vx933HFx2mmnxcUXX9ywA0jVw3GhDtULrFq1quj+waBg1dtZojEF8o6CvLOgS5cujQmg1gSqEFi/fn3RTVPv3r2rWEpWAo0rsHbt2lizZk0xEnzjKqg5gcoFVq9eXWTu0aNH5QvJSYBAzQS61mxNf15RBif+9V//NUaOHBl5q3Ze8bj77rtvdRN5Jeh73/vemDRpUjFA0lYzdvIZv/nNbyL77qzVaLwTJ04sRol/4YUXOrmM4hOoXCADOZMnT46nn3668oXkJNDAAgsWLCjazOzZsxtYQdUJVC4wa9asos1k25EIECgvkOdkeW6W52gSAQLlBfKCqHxIBAi0j0BNA6AZkOvfv3/MmDEjzj///OjXr1/ZWmU/mNnPZY6IXq8pBz/ad999a1q9gw8+OObOnVvTdVoZgY4skD+wrFu3LlasWNGRi6lsBDqMQOkLqTbTYXaJgnRwgVKbKU07eHEVj0C7C+TnS56b5TmaRIBAeYH8fPEZU95JDgKtJVDTAGiOTD569OiqboN45ZVXirr9+te/bq06tvt6c6CnWl61lrdoPfvss3HAAQe0e90UgAABAgQIECBAgAABAgQIECBAgEBHFqhpAHTEiBExf/78iuubvxg+8cQTRf4333yz4uU6W8a8WvO73/1u3HXXXTUp+pe+9KXIfkPyaluJQKMIdO36l7er0rRR6q2eBFoqUGorpWlL12M5Ao0iUGorpWmj1Fs9CbRUoNRWStOWrsdyBBpFINuK9tIoe1s9O6JATQdBykEWBg8eHD/84Q/jlFNOKVvfiy66KBYuXFjkGzduXNn8nTVDBoY/+MEPFgM9nXzyyUV/oIcffngxmEuldcpOxh955JG4+uqri752so9ViUAjCfTt27e4wrySrjUayUVdCWxNIAfOy872cyoRIFBeYNSoUbH99ts32399+bXIQaBxBHJQ2yVLlkSeo0kECJQXqOeYR/nay0Gg/QVqGgDN6lx55ZVxzDHHFMG9D3/4w03W8E9/+lN88YtfjG9/+9vF/BzR+QMf+ECTeevlxRzxPvtG/dGPflQ8MoiT/YLmgFHveMc7ihPuPn36RI48mqMp5hWx2a9OdhGQo8j/9re/jXTL9LnPfa7oY7VebNSDQKUCw4cPrzSrfAQaXiC7pckf4CQCBCoTyHMwbaYyK7kIpEDejeaONMcCgcoFBg0aVHlmOQkQqLlAzQOghxxySHzhC1+IE088McaOHRt5xeOee+4ZO++8c/zhD3+I6dOnx6233loMfFSqzWc/+9kib+n/epwOHDiwGPEtR4O/5557il9L8/b/UhcAldQ5T8wvueSS+PKXv1xJdnkIECBAgAABAgQIECBAgAABAgQINLxAzQOgKZpBurxlO/uqvPzyy5tF/uhHP1oETJvNVCczs3uAu+++O+6///647bbbikDosmXLytZul112iQsvvDAuuOCCyOcSAQIECBAgQIAAAQIECBAgQIAAAQKVCbRKADQ3/ZnPfCY+8pGPRF7d+fDDD28yOFJeyZgjmH/ta1+L7Auz0dKxxx4b+XjrrbfioYceitmzZxc+r776anFlbAZKhw0bVjyGDh0aBx10UHFrfKM5qS8BAgQIECBAgAABAgQIECBAgACBbRVotQBoFmyPPfYornTM54sWLYqZM2dG9nuR/fgZ/SyiV69ecdxxxyWPRIAAAQIECBAgQIAAAQIECBAgQIBAKwh0bYV1NrnKnXbaKbJ/0OwPdPPgZ45Sm1dCSgQIEGhO4MUXX4zFixc3l8U8AgT+UyAH1JszZ06sXLmSCQECFQhkW8k2k21HIkCgvECek+W5mUSAQGUCecdnPiQCBNpHoM0CoM1Vb/LkyfH44483l8U8AgQaXGDp0qUxbdq0YiC1BqdQfQIVCcybNy+ee+65opuVihaQiUCDC2SXRNlmsu1IBAiUF8jBbfPcLM/RJAIEygtMmTIl8iERINA+Aq1yC/y6devi17/+dTz//PPFbe/NXX2SeTL4mX2FSgQIENiaQL6vZCpNt5bP6wQI/EWg1FZKUy4ECDQvUGorpWnzuc0lQKDUVkpTIgQINC+wfv365jOYS4BAqwrUPAD6+uuvx+mnn+6W9lbdbVZOgAABAgQIECBAgAABAgQIECBAgEAlAjW/Bf7MM88U/KxEXh4CBKoS6NmzZ9F/cJ8+fapaTmYCjSpQaiulaaM6qDeBSgVKbaU0rXQ5+Qg0qkC2lRzbIc/RJAIEygv07t078iERINA+AjW9AjQ7wn7kkUeKmpx66qlxwgknxMEHHxw77rhjk7XLTuazn6XPf/7zTc73IgECBEoCebIwceLE6NGjR+klUwIEmhHYdddd45hjjvHFtBkjswhsLDBq1KgYNmxY9OrVa+OXPSdAYCsCY8eOjRzMVgB0K0BeJrCZwJFHHrnZK/4lQKAtBWoaAH377beL/vnGjRsX//Zv/1ZRPXbbbbe46qqr4s4776wov0wECDSugBPsxt33at4yAYGclrlZqjEFunTpIvjZmLterVso4OrPFsJZrGEFXMjRsLtexTuIQE0DoHm1SQY/99xzz6qqd8ABB1S9TFUbkJkAAQIECBAgQIAAAQIECBAgQIAAgYYUqHkfoHnr+4MPPhh5e3s1ac6cOdVkl5cAAQIECBAgQIAAAQIECBAgQIAAAQJlBWoeAL3oootiv/32i1tuuaXsxksZZsyYEXfffXfpX1MCBAgQIECAAAECBAgQIECAAAECBAjURKCmt8BnH6CzZ8+OK6+8Mk477bTiKtC8Lb659NJLL8Vtt91WDJjUXD7zCBAgQIAAAQIECBAgQIAAAQIECBAgUK1ATQOgK1eujAMPPDAyEJrpwgsvrLg8OWK8RIAAga0JZLcaU6ZMifxRZeTIkVvL5nUCBP5TYPny5fHMM8/E3nvvHYMGDeJCgEAZgQULFsTMmTOLc9kddtihTG6zCRDILsxee+21YgyI7t1r+rUSLoG6FJg6dWpRrzFjxtRl/VSKQEcXqOkt8P369Yv3ve99Hb3OykeAQCcUyGDO66+/HnPnzu2EpVdkAm0vsHDhwli0aFHMnz+/7TduiwQ6oUC2lWwz2XYkAgTKC+Q5WZ6b5TmaRIBAeYGXX3458iERINA+AjX/qe7EE0+Mn//853HSSScVvwb27t17qzVbv359cZJ5++23bzWPGQQIECBAgAABAgQI1L/AunXr4pVXXomcbpyWLVsWffr0iW7dum38cuSVujvvvPMmr/mHAAECBAgQINCUQM0DoMcff3y8853vjDvuuCN69OjR1Da3eG3ChAnx+OOPb/G6FwgQIECAAAECBAgQaAyBHEz129/+dsWVzYBodo+TXXBJBAgQIECAAIHmBGoeAB0wYEBcf/31FQc/s3BHHXVU9O/fv7lymkeAQIML5FUe2f9nuYHVGpxJ9QlsEBg4cGDkZ/KQIUM2vOYJAQJbF8i2klcaZtuR2kdg3333jT333HOTK0BXrVoVr776amy33XYxePDgTQq200472V+biLTtP0OHDo2ePXsWV+K27ZZtjUDnFBg+fHjnLLhSE6gTgZoHQNPl/e9/f7M8Tz75ZDEoQ9++fYt8Xbt2jbFjxza7jJkECDS2QHauP378+MZGUHsCVQjkjwZ5h4VEgEBlAn5kq8ypNXNdcMEFkY+N09NPP118T3j3u98dTz311MazPG9ngREjRkQ+JAIEKhMYPXp0ZRnlIkCgVQRqOgjSxiVcsmRJfP3rX48zzjgjLrvsso1nRY4WnyOfXX311Zu87h8CBAgQIECAAAECBAgQIECAAAECBAjUUqBVrgB9+OGHY9KkSRtGa85+QTdOhx12WNx3333xgQ98IKZOnRq33HJLcfvExnk8J0CAAAECBAgQIECAAAECBAgQIECAwLYK1DwA+vLLL8cJJ5wQy5cvb7Zse+21V/ziF7+IkSNHxqBBg+Laa69tNr+ZBAgQIECAAAECBAgQIECAAAECBAgQqFag5rfAX3LJJUXws0+fPjFx4sQ45ZRTtlqm7Gw+b5G/7rrrihEct5rRDAIECBAgQIAAAQIECBAgQIAAAQIECLRAoOYB0IceeigOPPDA4vb3Bx54IE4//fRmi7XffvsVIz3ecccdzeYzkwABAjkS7Nq1a0EQIFChQPa5vX79+gpzy0agsQWyrWSbkQgQqEwgz8ny3EwiQKAygdWrV0c+JAIE2kegpgHQefPmRQ5+dP3118dOO+1UUY0WLVpU5Js+fXpF+WUiQKAxBfJL6eTJkyNHg5UIECgvsGDBgqLNzJ49u3xmOQgQiFmzZhVtJtuORIBAeYE8J8tzMz8clLeSg0AKPProo8WDBgEC7SNQ0wDorrvuGr17947999+/4to88cQTRd7FixdXvIyMBAg0nkBeYbBu3bpYsWJF41VejQm0QKD0hVSbaQGeRRpSoNRmStOGRFBpAlUI5OdLnpu5CrQKNFkbWiA/X3zGNPQhoPLtLFDTAGj37t1jzJgxMWPGjP/P3n3AR1GtDx9/EggJLfQWIKEF5aIQqiiCIBgsCFfBjl6EqyBiQ6+KXcFerigicBVRsCBir1QRRQTpICWhSgvFEFqABHjf59z/5IZks7O7mc0mO7/z+QyzO3Nm5pzvcrKzZ07xKVuff/65zJgxw8Rt27atT8cQCQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8FXA0QpQvWjfvn3liSeekKNHj3pNwxdffCH9+/fPidOhQ4ec17xAAAEE8gpERv73z5W1zruf9wggcLqAVVas9el7eYcAAnkFrLJirfPu5z0CCJwuYJUVa336Xt4hgEBeAS0rlJe8KrxHoOgESjt9qbvvvlvmzZsnnTt3lsGDB0tERIS5xMGDB2XdunWyZs0aGTt2rFhd33Vn9+7d5aabbnI6KZwPAQTCSCA2NlaSkpKkUqVKYZQrsoJA8ATi4uLMQPu6JiCAgL1AYmKilC9fXurWrWsfmRgIIGCGPdP5H/QejYAAAvYC7du3t49EDAQQCJqA4xWgWuH57rvvylVXXSUDBw40Cdeu8QV9MeqM8Z988oloHAICCCDgTSAhIcHbbvYhgEAuAf1ebdy4ca4tvEQAAW8COo49ZcabEPsQOF2gcuXKogsBAQR8E6hVq5ZvEYmFAAJBEXC8C7ymsmLFivL999/Ld999Jy1btpTs7Ox8iT/jjDNk8uTJsmjRIlp05dNhAwIIIIAAAggggAACCCCAAAIIIIAAAgg4IRDUZpcXX3yx6KLd39euXSubN2+WOnXqSNOmTUVnjCcggAACCCCAAAIIIIAAAggggAACCCCAAALBFAhqBaiVcG0R2q5dO7NY26z19OnTpUGDBqZS1NrGGgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcEIgKF3g/UnYH3/8YbrC+3MMcRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAV8E/G4BmpKSIm+//bZERUWZpVSpUhIZGWkWvaBOgmTN/J73de79p06dkq1bt8rHH39sZtx86qmnfEkvcRBAwMUCOowGA+67+D8AWfdLQMff1jKjM1rr5C4EBBDwLpCZmSnbt283PZOYnNO7FXsRUIH9+/ebRXvzERBAwF5g165dJlLt2rXtIxMDAQQcF/C7AjQuLk5ee+010ZtEJ8P8+fPlvPPOc/KUnAsBBMJI4MCBA7J8+XKpUqWKdO7cOYxyRlYQCI7Ajh07ZPXq1XLkyBFp0aJFcC7CWREIIwF9yL9p0yYpU6aMxMfHh1HOyAoCwRFYsWKFpKenS9WqVSU2NjY4F+GsCISRwMKFC01uevXqFUa5IisIlBwBv7vAly9fXi666CLHczhx4kTHz8kJEUAgfAROnjxpMmOtwydn5ASB4AhYZcVaB+cqnBWB8BGwyoq1Dp+ckRMEgiNglRVrHZyrcFYEwkdAe8HqQkAAgdAI+N0CVJN5ySWXyMaNG2Xs2LGma13upK9cuVKuvvpq+de//iVdu3aVhISEnO7xuePp69mzZ8vTTz8tb775prRs2TLvbt4jgAACCCCAAAIIIIAAAggggAACCCCAAAKFEgioArR79+5mXLGOHTvmu/h1110nH330kfTu3TvfvrwbBgwYYMYLHTx4sCxbtizvbt4jgAACOQLR0dHmYUq5cuVytvECAQQKFrDKirUuOCZ7EEBABayyYq1RQQAB7wJaVg4ePCh6j0ZAAAF7AcZktzciBgLBFAioArRJkybSv3//fOnSMfp00gVfKj+tg/v27Su33XabzJw5U6688kprM2sEEEDgNAG9YUhOTjaTr522gzcIIOBRoGbNmtKjRw9+mHrUYSMC+QUSExPN2J8xMTH5d7IFAQTyCbRt21aysrL4nsknwwYEPAtoD1kCAgiETsDvMUCtpJ555pnWy5z1+vXrRSdJ8ifomDH6xakVoAQEEEDAm4DVCtRbHPYhgMD/BLQiJyIi4n8beIUAAgUKaFmh8rNAHnYgkE8gMjKSys98KmxAoGCBqKgoGnMUzMMeBIIuEHAFqKeUNW/eXBYvXuxpV4Hb5s2bJ1oJqjNvEhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAScFHK0A1S6qdevWNRMb+ZLIDRs2yJAhQ0zU1q1b+3IIcRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZ8FHK0A1as+8sgj8thjj5lxPbWC01PQwbJ15vdOnTrJn3/+aaJ07tzZU1S2IYAAAggggAACCCCAAAIIIIAAAggggAACAQsENAmSt6tdfvnl8uyzz8oDDzwg48aNk/bt20ujRo3M2KB//fWXbNmyRRYtWmRmDLTOoxMqXXbZZdZb1ggggAACCCCAAAIIIIAAAggggAACCCCAgCMCjleAaqruv/9+MyD2k08+Kb/99ptZCkqtVpiOHTu2oN1sRwABBIxAdna2LFy4UHRm6yZNmqCCAAI2AocOHZIlS5bIGWecIbVq1bKJzW4EEEhLS5N169aJDstUoUIFQBBAwEYgNTVVdu/ebRq8lC4dlJ+VNilgNwIlS2Dp0qUmwa1atSpZCSe1CISJgONd4C2Xu+66S7QL/L333mt+fOX+UixTpoycc8458v3338uXX37J7IEWGmsEEChQQCtz9uzZI9u2bSswDjsQQOB/Anv37pX09HTZuXPn/zbyCgEEChTQsqJlRssOAQEE7AX0nkzvzfQejYAAAvYCW7duFV0ICCAQGoGgPqqrUqWKvPTSS2bJysoyFaKRkZHSuHFjKVWqVGhyzFURQAABBBBAAAEEEEAAAQQQQAABBBBAwDUCQa0Aza0YFRUlZ555Zu5Np73Wp+516tQ5bRtvEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBwggErQu8P4nKyMiQ8ePH+3MIcRFAwGUCOh6bjv9Zv359l+Wc7CIQmED16tWlatWqZhLCwM7AUQi4SyAuLs6UGS07BAQQsBfQezK9N2PMXHsrYiCgAgkJCWZBAwEEQiNQZC1AC8reqVOnTOWnrgkIIIBAQQI6jvC5555b0G62I4BAHgH9QdqpU6c8W3mLAAIFCWhFji4EBBDwTUCHNdOFgAACvgkkJSX5FpFYCCAQFIGAKkAfe+wx+fHHH+XRRx+Viy66KCdh+/fvl44dO8qJEydytnl7oeOC6kDzBw4ckMcff9xbVPYhgAACCCCAAAIIIIAAAggggAACCCCAAAJ+C/hdAbpixQoZMWKEuZDO8K7vrVC5cmXT1W7mzJnWJtYIIIAAAggggAACCCCAAAIIIIAAAggggEDIBPyuAG3YsKFUqlRJdNxOT024b7rpJtEK0LJly5rK0JiYmAIzpy1A9+zZI+np6QXGYQcCCCCAAAIIIIAAAggggAACCCCAAAIIIBCogN8VoBUrVpSNGzfKsmXL5IILLsh33d69e5vxk5YsWSJ169bNt9/ThhdffFEOHz7saRfbEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBgAUCmgVeZ5XVyk1PExfFxsZK//79fa781JTffPPNAWeAAxFAwD0Cx44d83mMYfeokFMEChbIzMz0+F1d8BHsQcC9Anpfq2WGgAACvgnovA96b0ZAAAHfBLQHrC4EBBAIjUBAFaAPPfSQnHnmmadNgJQ7+XfccUfut7avq1evLn379rWNRwQEEHCvgP4onT59uixevNi9COQcAT8E0tLSTJlJSUnx4yiiIuBegfXr15syo2WHgAAC9gJ6T6b3Zjw4sLciBgIqMGfOHLOggQACoRHwuwJUn46PGTPGpPavv/7ymOp69ep53O5t41lnneVtN/sQQMDlAtrC4OTJk3LkyBGXS5B9BHwTsH6QUmZ88yIWAlaZsdaIIICAdwH9ftF7M1qBendiLwKWgH6/8B1jabBGoOgF/K4AXbNmjZkAqVOnTvL999/nS3H79u1ly5Yt+bazAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKGoBvytAN2zYIK1atZJZs2ZJnTp18qX3zz//9Htci+zsbHnhhRfynYsNCCCAgCUQGfnfP1fW2trOGgEEPAtYZcVae47FVgQQsASssmKtre2sEUDAs4BVVqy151hsDZWA/sYmFC8BLSuUl+L1mZAadwn4PQt8fHy8NGzYUKKiohyTSk1NpVurY5qcCIHwFNAJ1pKSkqRSpUrhmUFyhYDDAnFxceaBpK4JCCBgL5CYmCjly5f3ayJP+7MSA4HwFWjRooXpGaj3aITiI7B8+XKTmGXLlhWfRJESI6C9ZQkIIBA6Ab9bgOoX3ebNm+Wdd96RvXv3Firl+lRq69at8sQTTxTqPByMAALuEEhISJDKlSu7I7PkEoFCCpQuXVoaN24sZcuWLeSZOBwBdwhoWdEyU6pUKXdkmFwiUEgBvSfTezNC8RE4fvy4TJ061SRIK0L1tzah+AjUqlVLdCEggEBoBPyuAI2IiJBXXnlFbr/9dqlRo4aUK1dOKlSokLPozJk6Q7z+8LJbypQpY740p0yZEprcc1UEEEAAAQQQQAABBBBAAAEEwkBg1KhRsmfPHpOTEydOyP333x8GuSILCCCAgDMCfleA6mUvuOACmTt3rpx33nlmFrPDhw+Ltegs8frH1pdF4xIQQAABBBBAAAEEEEAAAQQQQCBwAW2INHLkyNNOoA2Nfv7559O28QYBBBBwq4DfY4BaUO3atZNffvlF9A+tzgz/xx9/mPVbb70lXbp0MWMoWXELWh87dszMGL9y5cqCorAdAQQQQAABBBBAAAEEEEAAAQS8CDz00ENy4MCBfDHuuusuWbRoEZPv5JNhAwIIuE0g4ApQC8oax0IrPTV88skn8vrrr0uTJk3Me1/+GT16dKHHE/XlOsRBAAEEEEAAAQQQQAABBBBAIJwElixZIhMnTvSYJd03YcIE+ec//+lxPxsRQAABtwgE1AXeaZwbb7xRdGxRAgIIIOBNQCdg279/v7co7EMAgf8T0IkGU1NTzVA1oCCAgL1AZmamKTNadggIIGAvoPdkem9GCL2AtvI8efJkgQl5+OGHPbYOLfAAdgRFYNeuXaILAQEEQiPgeAXo2LFjpXbt2n7lplKlStKnTx+/jiEyAgi4S0C79OhslitWrHBXxsktAgEK7NixQ1avXi0pKSkBnoHDEHCXgJYVLTNadggIIGAvoPdkem/mqdu1/dHEcErgo48+sh3nc/fu3TJixAinLsl5AhRYuHCh6EJAAIHQCDheAdq7d28zI7w/2cnKyuJJiD9gxEXAhQLWU21r7UICsoyAXwJWWbHWfh1MZARcKGCVFWvtQgKyjIBfAlZZsdZ+HUxkRwS05bqvM72/9tprPBR1RD3wk+gk0EwEHbgfRyJQWAHHK0ADSdD06dNtn1oFcl6OQQABBBBAAAEEEEAAAQQQQCAcBZ5//nn5888/fcra8ePHZdiwYT7FJRICCCAQjgKFngTJE4o+Bfz111/NrPDr1q3zOv6YziD/888/y/Dhwz2dim0IIICAEYiOjjazV5YrVw4RBBDwQcAqK9bah0OIgoCrBayyYq1djUHmEfBBQMvKwYMHRe/RCEUvoBWfL7zwgl8X/vrrr0UbHyUnJ/t1HJGdEShbtqwzJ+IsCCAQkIDjFaB79uyR66+/XmbOnBlQgjgIAQQQ8CSgNwx6sxYVFeVpN9sQQCCPQM2aNaVHjx78MM3jwlsEChJITEyU+Ph4iYmJKSgK2xFAIJdA27ZtRYcyowI0F0oRvtSu79oF3t9w9913mzH1S5d2vCrA36S4Ln7Xrl1dl2cyjEBxEnC8C3y/fv2o/CxOnzBpQSCMBKxWoGGUJbKCQFAFtCInIiIiqNfg5AiEi4CWFSo/w+XTJB9FIRAZGUnlZ1FAe7jGL7/8Ijr5USBBe2COGTMmkEM5ppAC2pCDxhyFRORwBAoh4GgF6P79+2X27NkmOddcc418+OGHsnHjRtFWoZ6WnTt3yuLFi+WSSy4pRBY4FAEEEEAgFALa6kBvvq2xp9577z3ZsGFDKJLCNRFAAAEEEEAAAVcI6HBzd911V6Hy+sQTT8i+ffsKdQ4ORgABBEqagKMVoDqwsv5Bbt++vflRfO2110rDhg2levXqHpfatWtL69at5dlnny1pbqQXAQQQcLWAjh/VuHFjue6662T37t3GQmcXbdq0qWjXKu0SR0AAAQQQQAABBBBwVmDixImmEVFhzpqeni6PPfZYYU7BsQgggECJE3B04A8db0wrP7XS05/QsmVLv4/x5/zERQABBBBwTmDGjBmm5b4+8MobdNuoUaMkLS3N9ALIu5/3CCCAAAIIIIAAAoEJ6H3WnDlzpHv37gWeYNu2bbJ27VqpW7euNGvWrMB42lNTW4FWq1atwDjsQAABBMJJwNEKUIXRru9PP/20ZGdniz8DK6empprWoOGES14QQACBcBM4evSoDBgwwLT295Y37Rp/9dVXyxVXXOEtGvsQQAABBBBAAAEEfBTQcVcnTZrkNfb48eNl0KBBctlll8m4ceO8xmUnAggg4CYBR7vAK9wdd9whZ511lkyYMMFnx1WrVsmXX37pc3wiIoCA+wT0ocr8+fNFH5YQQifwzTffiLYs8CWMHTvWl2jECZLAoUOH5KeffjKtcYN0CU6LQFgJaMt1LTNadggIIGAvoPdkem+m92gEBBCwF1i6dKnoQkAAgdAIONoCVMcATUlJkREjRoiO/6lfhtot3lvYsmWLeYr197//3Vs09iGAgMsF9AepTqamf2eaNGnico3QZX/RokU+X9yfuD6flIg+C+zdu1d0jC+dcLBWrVo+H0dEBNwqoGVFy4yWnQoVKriVgXwj4LOAPhDNyMgwDw0qV67s83FERMCtAlu3bjVZb9WqlVsJyDcCIRVwtAJUZwTWSY20gkLD7bff7nPmqAD1mYqICCCAQMgEtAu8r8GfuL6ek3gIIIAAAggggAACCCCAAAII+CvgaBf4SpUqyUUXXeRvGoiPAAIIIFBCBBITE31OqT9xfT4pERFAAAEEEEAAAQQQQAABBBDwU8DRFqB67T59+oiOEde3b18zI3zZsmULTNKpU6dMN6PJkycXGIcdCCCAgApod0QdUsNuWA20givQu3dvueeeeyQrK8v2QjoJEiF0AtWrV5eqVatKXFxc6BLBlREoQQJaVg4ePChadggIIGAvUL9+fYmOjmbICHsqYiBgBBISEpBAAIEQCjheAao/jhs1aiQffPCBREVF+ZS1Tp06yc8//+xTXCIhgIA7BUqXLi3nnnuuOzNfjHJdr149eeCBB2TkyJFeU9W4cWO56667vMZhZ3AF9KGBfr8SEEDANwEesvnmRCwELAH9rteFgAACvgkkJSX5FpFYCCAQFAHHK0C1tcno0aN9rvzUXF144YXCwNlB+Xw5KQIIIOC4wJNPPim7d++W8ePHezy3/hj69ttvaRHiUYeNCCCAAAIIIIAAAggggAACRS3g6BigVuIvueQS6+Vpa+3y7ilERkZK27ZtPe1iGwIIIIBAMRPQv9njxo2TH374QXQCO6u1/5lnninPPfecLF++XJo2bVrMUk1yEEAAAQQQQAABBBBAAAEE3CrgeAvQ3JA7duyQl19+WVasWCGrV6+Wffv2mR/FZ511lmhX+WuvvTZ3dF4jgAACCJQggeTkZNFFH2AtXrxYdDznNm3alKAckFQEEEAAAQQQQAABBBBAAAE3CASlAlRbev7nP/+R+++/XzIyMk5zXLVqlejy0UcfyZgxY+T111+Xli1bnhaHNwgggAACCCCAAAIIIIAAAggggAACCCCAgBMCQekCr60+Bw0alK/ys1y5clK7dm0pVaqUSfu8efOkY8eOsmbNGifywjkQQCDMBY4dOyYnTpwI81ySPQScE8jMzJSChp9x7iqcCYHwENCyomWGgAACvgnoPZnemxEQQMA3gaysLNGFgAACoRFwvAXounXr5NFHHzW5qVKlilxzzTVy3XXXSfPmzaVatWpmu35Zbt++XaZNmyZjx46Vvn37ysKFC6V8+fKhUQjRVY8cOWImCklNTZVt27YZkwMHDhin6tWrS4MGDaRLly6mS6lVaRyipHJZBEIuoD9KZ86cKbVq1ZL27duHPD0kAIHiLpCWliYLFiyQZs2aMSZrcf+wSF+xEFi/fr2sXbtWOnToYL5rikWiSAQCxVhAh7/R75ru3btL2bJli3FKSRoCxUNgzpw5JiE6hBQBAQSKXsDxCtDHHntMjh49KjfddJOZJCMmJiZfrrQyLz4+Xu655x655ZZb5PLLL5c33njDdJnPFzkMN8yePVsmTpwon332mRw6dMg2h7GxsaaS+IEHHuBHrK0WEcJVQFsYnDx5UvTBAQEBBOwFrJZslBl7K2IgoAJWmbHWqCCAgHcB/X7RezO9R6MC1LsVexFQAb5f+H+AQGgFHO8C//vvv4tW1L377rviqfIzb3YrVKggo0ePNi1A8+4Lt/e7d++WPn36SLdu3WTSpEk+VX6qgbYKnTBhgmnFM2TIEMnOzg43GvKDAAIIIIAAAggggAACCCCAAAIIIIBAUAQcbQGqrRk3b94sd955p1+J/dvf/mYmRvLroBIWee/evdK5c2fRIQI0aAvYs88+WxITE6V+/fqi46PqopXG+mTo4MGDZtHKTz1GhwjQYQPefPNN2bBhg3z11VdSpkyZEqZAchEIXCAy8r/Pa6x14GfiSATcIWCVFWvtjlyTSwQCF7DKirUO/EwciYA7BKyyYq3dkWtyiUDgApSVwO04EgEnBBytANWKO63Qi4uL8yttOn7M4cOH/TqmpEUeOHCg6FifQ4cONd3+W7Ro4XcWduzYIc8995wZLkBbzQ4bNszvc3AAAiVVQIeCSEpKkkqVKpXULJBuBIpUQL+LdaB9f7+TizSRXAyBYiSg97A6Hn3dunWLUapICgLFV0B/z2RkZIjeoxEQQMBegHkM7I2IgUAwBRytAK1Ro4boUw39wRUVFeVTunXGTZ0IqXXr1j7FL4mRdJb7H374QXTQ406dOgWcBf0R+9prr0nPnj3l2muvlbvvvtt4B3xCDkSghAkkJCSUsBSTXARCJ1C6dGlp3Lhx6BLAlREoYQI6hiFlpoR9aCQ3pAKVK1cWXQgIIOCbgE7mSkAAgdAJOD4GaKNGjeSpp57yKUfaYlRniX/77bfDelbn3377TQYNGlSoys/coDprnM4Sv2nTptybeY0AAggggAACCCCAAAIIIIAAAggggAACeQQcrwDVCZCeeeYZ6dq1qyxZskROnDiR55Iiq1evln/961+ilaVTp06VBg0amK7h+SKGyQad/Kh58+aO5uacc86Rbdu2OXpOToYAAggggAACCCCAAAIIIIAAAggggEC4CTjaBV5xtIv3Qw89JCNHjpQ2bdqIdsHTCX/q1Kkje/bska1bt8rRo0dzHEuVKiWTJ08O63H96tWrJ3Pnzs3Jc2Ff6LABy5Ytk5YtWxb2VByPAAIIIIAAAggggAACCCCAAAIIIIBAWAs43gJUtR5//HEZPny4GQc0OztbNm7cKL/88ousX7/+tMpPbfn59ddfS8eOHcMaWVtrvvPOO/L55587ks8nn3zS2DLmjiOcnAQBBBBAAAEEEEAAAQQQQAABBBBAIIwFglIBqq0+tRv80qVL5YorrpD69evnEOoA8zrhkbYS1a7wF198cc6+cH2hA+r36tXLWFx99dUya9Ys0Yphf4IOJTBjxgzp0aOHaAXorbfe6s/hxEUgLAQ2b94s+/fvD4u8kAkEgi2g3zOpqami420TEEDAXkDLipYZf+/R7M9MDATCU0DvyfTejIAAAr4J7Nq1S3QhIIBAaAQc7wKfOxs67uWnn35qNmVkZMi+ffvMeJ86U7zbgs50v2rVKjPmqY57WqlSJTMuaJMmTYxJ+fLlpVy5cqIVxHrjffjwYTly5Ihs375ddBb5lStXyl9//WXYHn74YRk8eLDbCMmvywUOHDggy5cvlypVqkjnzp1drkH2EbAX2LFjh3nQqN8lLVq0sD+AGAi4XCAlJcVMMFmmTBkzfJPLOcg+ArYCK1askPT0dKlatarExsbaxicCAm4XWLhwoSHQxlEEBBAoeoGgVoDmzo5W+OliBf1BpjeY2lrUDUFnbZ8zZ46ZDf6rr74SrRCeP3++WXzNv1aODhs2zIyv6usxxEMgXAROnjxpsmKtwyVf5AOBYAlYZcVaB+s6nBeBcBGwyoq1Dpd8kQ8EgiVglRVrHazrcF4EwkVA5/IgIIBA6AT8rn08fvy4mcHdaqGYd/3NN99I3bp1bXM0ceJE+f3332XChAm2ccMlgk4E9eWXX8r3338vkyZNEq0IPXjwoG32atSoIbfffrsMGTJE9DUBAQQQQAABBBBAAAEEEEAAAQQQQAABBHwT8LsCNCoqSrQL+9tvv51zhQoVKsidd94pAwYM8KnyUw8cNGiQTJs2TR555BHXtWjUcU91OXr0qMycOVO0y9XOnTvNeCDazVcrSuPj482i46e2a9fOdI3PAecFAi4UiI6ONn97dKgIAgII2AtYZcVa2x9BDATcLWCVFWvtbg1yj4C9gJYVbcyh92gEBBCwF9AenQQEEAidgN8VoBEREaJjUL766qsm1ToW3/vvvy/16tXzKxelSpWSDz/80EyIlJSUJH379vXr+HCIHBMTIz179gyHrJAHBIIuoDcMycnJog9hCAggYC9Qs2ZNM3EeP0ztrYiBgAokJiaah896f0ZAAAF7gbZt20pWVhYVoPZUxEDACHTt2hUJBBAIoUBAsxGtXbvWJLlRo0by2Wef+V35aeVXf5w99thjMmLECGsTaxuBf//732bSJCZBsoFid1gKWK1AwzJzZAqBIAhoRY4+uCQggIC9gJYVKj/tnYiBgCWgvQJ5yGZpsEbAXkAbctCYw96JGAgESyCgCtDZs2ebGcs///xzM+tfYRJ3/fXXmxk3dVxMgneB/fv3y4MPPihbtmyRcePGyc8//+z9APYigAACCCCAAAIIIIAAAggggAACCCDgcoGI/z8Tmd9TkV1zzTWikx99/fXXjvBdeeWVUr58eTMxkCMnDNOT6AyLTZo0MRXG+uRIK0J1vNBgh+HDh8vzzz8vAfxXCXbSOD8CCCCAAAIIIIAAAggggAACCCAQ9gI6mXa/fv3CPp/BymBALUB10h6dmMep0LBhQ1m/fr1Tpwvb82g3k7lz58qLL74oCxcuLJLKT8Wk4jNs/0uRMQQQQAABBBBAAAEEEEAAAQQQQCDsBfyeBElFtAK0TZs2juHUrl1bUlNTHTtfOJ9IZ4W/7777ijSLzz33nOjiRGjdurUsXbpU/v73v5vxY504J+dAAIHQCugkCIsXL5bff//d0e+G0OaKqyMQPAFrXFYeMAbPmDOHj4B+v+j3jP720O8ZAgIIeBcYP368DBo0SG699VYzbJr32OxFAIGSIHDjjTfK5MmTS0JSi3Ua/W4Bmp2dbbq/Z2RkOJaxdevWSXp6umgXbwICCCDgSUD/9syfP5+HJZ5w2IaAB4FDhw7JTz/9JGlpaR72sgkBBPIKaFnRMqNlh4AAAvYC2oBF7830Ho2AAAL2AtoQSBcCAgiERsDvCtDSpUtLlSpVZMGCBY6lWJ/mVq1aVbSLNwEBBBDwJKA/SPfs2SPbtm3ztJttCCCQR2Dv3r3m4eLOnTvz7OEtAgh4EtCyog/ktewQEEDAXkDvyfTejIcG9lbEQEAFtm7dahY0EEAgNAIB1TjWq1fPsQpQbUm6evXqIhvPMjTMXBUBBBBAAAEEEEAAAQQQQAABBBBAAAEEQiEQ0Big3bp1k3//+99m1nYdi6AwQccm0W4TnTp1KsxpSuSxR44ckW+/rN7opgAAQABJREFU/dZ06dUnqNu3b5cDBw5ItWrVpHr16tKgQQPp0qWLGfOoVKlSJTKPJBoBBBBAAAEEEEAAAX8Fjh8/Lm+//bZMmDDBHLpq1Sq56aab5LbbbpNzzz3X39MRHwEEEEAAAQRcLhBQBWivXr1MBahWXp5xxhnSvn37gBjHjBkjH3/8sTn2kksuCegcJfGg2bNny8SJE80kQL50GYmNjZW+ffvKAw88IE2bNi2JWSbNCBRaoEKFClKzZk2zFPpknAABFwjogzQdXiYuLs4FuSWLCBReQMvKwYMHzUPowp+NMxRGYMuWLdKzZ0/RSk8rHDt2zDS+mDRpktx7773y4osvijWhmBWHddEK6OSs0dHRovdoBAQQsBdISEiwj0QMBBAImkBAXeC1VeL5558vR48eNbN5f/LJJ+LPTKZ6A/PCCy/IsGHDTMaaNWsml156adAyWVxOvHv3bunTp49oC1q9efOl8lPTrq1C9em3Og0ZMoSBxovLB0o6ilRAxx/WFh+NGzcu0utyMQRKqoD+INXeFfrggIAAAvYCWla0zFCZY28VzBiZmZmiDSNyV37mvd7LL78szz33XN7NvC9iAb0n03szvUcjIICAvUBSUpLoQkAAgdAIBPxt9corr0jHjh1FB4y/6qqrpEWLFvLEE09I7969C5zMSLu6f/rpp/Lggw/Kpk2bcnKsNzDh3sVbB9Tv3Lmz6Iz3GuLj4+Xss8+WxMRE0aen5cqVM0tMTIzojZ+2QNBFKz/1mIULF5ou8m+++aZs2LBBvvrqKylTpkyOIS8QQAABBBBAAAEEECjpAqNHj5Y1a9bYZuPJJ5+Um2++WWrXrm0blwgIIIAAAggggEDAFaDt2rWT//znP9K/f3+juGLFCrnyyislKirKVO41bNhQdNGKvdTUVFm/fr2p9NRK0NxBK0O1S324h4EDBxqHoUOHyi233GIqjP3N844dO8zT7jfeeEP05tBqQevveYiPAAIIIIAAAqERmD59es6F9XVycnLOe14ggIDIhx9+6BOD9ij7/PPPZfDgwT7FJxICCCCAAAIIuFsg4ApQZfvHP/5hxt6555575K+//jKSWVlZpoWitlL0FrSrhI7f88wzz3iLFhb79Cn2Dz/8IHPmzCnUZE86NtVrr71mxkS69tpr5e677y6wtW1YwJEJBBBAAAEEwkhAHwLrd7cV9LU+QKb7qCXCGgHJ6S3li4XVs8qXuMRBAAEEEEAAAXcLBDQGaG4ynY1x7dq14s9s8Np1fsmSJaY1oxsGL//tt99k0KBBhar8zG2urUV0covcwwjk3s9rBBBAAAEEECh+Ajr5Y+6uvfpatxEQQOB/AtqbzNfAcFC+ShEPAQQQQAABBApdAaqENWrUkPfee090dvPbb79dLrjgAqlWrVqObtmyZaVVq1bSr18/E2/evHlm/MucCGH+Qic/at68uaO5POecc2Tbtm2OnpOTIVDcBbS724kTJ4p7MkkfAsVGQMeU9meSwmKT8DBMyL59+8xY6XmzpuOn6z5C6AW0rGiZIYRWoHXr1j4nwJ+4Pp+UiD4L6D2Z3psREEDANwHtLasLAQEEQiNQqC7weZPctWtX0cUKu3btksOHD5uxQCMjHalrtU5dotb16tWTuXPnOpZmvUFftmyZtGzZ0rFzciIEiruA/iidOXOm1KpVS9q3b1/ck0v6EAi5QFpamixYsECaNWsmTZs2DXl63J6ARx99VNLT0/Mx6DbdR0vQfDRFvkHHq9deTR06dDDfNUWeAC5oBHSsfB02yi7o/cDll19uF439QRRYvHix6HdN9+7dRRu8EBBAwLuA9beN8b+9O7EXgWAJBLVWUmdlbNy4sevHqdTWmu+8844ZqN2JD1JnvdTuQZUrV3bidJwDgRIhoC0MTp48KUeOHCkR6SWRCIRawGrJRpkJ9SchsnLlShk/fnyBCdF9GocQWgGrzFjr0KbGvVfXce59mSBVy41OtkoInYB+v+i9Ga1AQ/cZcOWSJaDfL3zHlKzPjNSGl0BQK0DDiyrw3GglsN7IXXHFFXL11VfLrFmzRCdC8CdoF5MZM2ZIjx49RCtAb731Vn8OJy4CCCCAAAIIhEhAJzvyNnyH7ss9OVKIksllESgWAjo/wJQpU+Tmm2/2mJ6qVavKtGnTfKok9XgCNiKAAAIIIICAKwUc7QLvSkEfMz127FhZtWqVTJ061SyVKlUy44I2adJEGjRoIOXLlzdPsbX7iFaO6tAB+lR1+/btZsIEbRny119/mas9/PDDMnjwYB+vTDQEwkPAGkbDWodHrsgFAsETsMqKtQ7elTizN4HPPvvMjJHuLY7u03HUNa4+LCWERsAqK9Y6NKngqioQExMjEyZMkLvuuktGjx4tb731lplf4JlnnpFrrrlG9D6aEHoBq6xY69CniBQgULwFKCvF+/MhdeEvQAVoEX3GOmu7jvmhs8F/9dVXkpGRIfPnzzeLr0nQytFhw4bJyJEjfT2EeAiEjUBsbKwkJSXxoydsPlEyEmyBuLg4M9C+rgmhEdBuoffdd5/PF9e4l156qURHR/t8DBGdE0hMTDQPpOvWrevcSTlToQR0vHt96K8VoNpggB5QheJ0/OAWLVqY3zR6j0ZAAAF7AeYxsDciBgLBFKALfDB185y7Tp068uWXX8p3330n119/vVSsWDFPDM9va9SoYWaO3bJlC5WfnonY6hKBhIQExr51yWdNNgsvULp0aTMONxNTFN4y0DO88sorsnHjRp8P17h6DCE0AlpWdNiiUqVKhSYBXBWBEiag8xHovRkBAQR8E9DJ23QhIIBAaARoARoC94svvlh0OXr0qJnVOiUlRXbu3Cm7du2SAwcOiFaUxsfHm6V+/frSrl07ZlYMwefEJRFAAAEEEAhUQL/Xtbuuv0GP6d+/v7kX8PdY4iOAAAIIIIAAAggggIBnASpAPbsUyVYd36hnz55Fci0uggACCCCAAAJFJzB8+HA5dOiQ3xfUY/TYiRMn+n0sByCAAAIIIIAAAggggIBnAbrAe3ZhKwIIIIAAAgggEJDAokWL5L333gvoWD1Ij9VzEBBAAAEEEEAAAQQQQMAZASpAnXHkLAgggAACCCCAgJw6dcrMXK3rQIMT5wj02hyHAAIIIIAAAggggEA4ClABGo6fKnlCIEwFNm/eLPv37w/T3JEtBJwVyM7OltTUVMnMzHT2xJzNq8AHH3wgv/76q9c4vuzUc+i5CEUnoGVFy4yWHQICCNgL6D2Z3psREEDANwGd80MXAgIIhEYg6GOATpkyRebNm2cm+UlMTJQWLVrIpZdeykzOofm8uSoCJVZAJwhbvny5VKlSRTp37lxi80HCESgqgR07dsjq1avlyJEj5ru3qK7r9utcfvnlsnv3bq8MNWvWNPvt4kVHR3s9DzudFdBJKTdt2iRlypQxE1E6e3bOhkD4CaxYsULS09OlatWqEhsbG34ZJEcIOCywcOFCc8ZevXo5fGZOhwACvggEtQJ0yJAhooX8xhtvlAEDBpgnhEuXLpWRI0fKlVdeKffcc49Uq1bNl3QSBwEEXC5w8uRJI2CtXc5B9hGwFbDKirW2PYAIjgj4UwlQo0YNR67JSZwRsMqKtXbmrJwFgfAVsMqKtQ7fnJIzBJwRKMzwOM6kgLMg4G6BoFWA6hP0CRMmmCfpderUMcqtW7c2FZ+PPPKIvPvuu9KxY0eZNGmStGvXzt2fArlHAAEEEEAAAQQQQAABBBBAAAEEEEAAgaAIBG0M0JUrV5qKTavyM3fqtUvXrbfeKt9++63cfvvt8vPPP+fezWsEEEAgn4D+3YiMjJRy5crl28cGBBDIL2CVFWudPwZbEEAgt4BVVqx17n28RgCB/AJaVvTejOE68tuwBQFPAmXLlhVdCAggEBoBv1uAHjt2TE6cOGFbCdGoUSNZsGCBGfvTUyWoZlfjfPfdd9KmTRvZuHGj+QINDQNXRQCB4i6gNwvJyckSFRVV3JNK+hAoFgI6zmSPHj34YVosPg0SURIEdKz6+Ph4iYmJKQnJJY0IhFygbdu2kpWVxfdMyD8JElBSBLp27VpSkko6EQhLAb9bgOqsZU2bNjVd2L2NYdG8eXMzGPbAgQPlr7/+KhBPxwDt0KGDLFmypMA47EAAAQRUwGoFigYCCPgmoBU5ERERvkUmFgIuF9CyQuWny/8TkH2/BGj96RcXkREwDTlozMF/BARCJ+B3Bag+GU9KSpL+/fublpuzZ8/2mHq9idRu7trCs2XLljJ9+nSP8XRjpUqVpKDzFHgQOxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAARsBvytAtWJz8uTJpouQdknt1q2b9OrVS9auXZvvUjrbu+7ftm2b6YbXokULefPNNyUtLS0n7vLly2Xq1KlSsWLFnG28QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEnBDwuwJUL1q5cmW54447TCtQbeGpM76fffbZMnToUNm7d29OukqVKiVTpkyRc845x2zTiZGGDBkitWvXFu36rmMtaWvSzMxMufjii3OO4wUCCCCAAAIIIIAAAggggAACCCCAAAIIIOCEQEAVoHrhq6++Wr744gtTcbls2TIZM2aMTJs2TZo0aSIvvPCC6GRJGrSic/78+fLKK6+cNnGSjguamppqxlp64403pGHDhiY+/yCAAAIIIIAAAggggAACCCCAAAIIIIAAAk4JBFwBWq9ePdmzZ49Jh7b0vOWWWyQlJUXuvPNOefLJJ+WMM86QDz/80OzXAbLvuece2bx5s6k0ffTRR01LUK0UXbVqlQwYMMCp/HAeBBAIU4Hs7GzzMEUfnBAQQMBe4NChQ/LTTz+dNuyM/VHEQMC9AjpEk5YZLTsEBBCwF9B7Mm3oovdoBAQQsBdYunSp6EJAAIHQCARcAaqVmjoGaO5QoUIFeeqpp2T9+vXSpUsX6devn+n+/ssvv5hoNWrUMOOFahxt9amVoo0bN859Cl4jgAACHgX0B6k+dNExhQkIIGAvoEPSpKeny86dO+0jEwMBBExZ0TKTezgnWBBAoGABvSfTezMeGhRsxB4Ecgts3bpVdCEggEBoBAKuAN2wYYNohac+KV+4cOFpX3x169aViRMnyuLFi02c888/X/r27St6DAEBBBBAAAEEEEAAAQQQQAABBBBAAAEEECgqgYArQLUbu3Zp10mOXnvtNTPbu87y/vTTT+dUhuoER7NmzZKvv/5a/vjjD/nb3/4mw4YNMy1SiiqDXAcBBBBAAAEEEEAAAQQQQAABBBBAAAEE3CsQUAXoJ598Ip9++ql8++23piv75MmT5bfffjNjwERFRUmHDh1k/PjxOaqXXXaZrFixQl599VV5//33zURJ+jorKysnDi8QQAABbwLa4rxmzZpSv359b9HYhwAC/ydQvXp1qVq1qsTFxWGCAAI+CGhZ0TKjZYeAAAL2AnpPpvdmeo9GQAABe4GEhATRhYAAAqERCKgCdN68eTJ06FCJj48/LdX65Xf//ffLggULzIzwOsmRFUqXLi233Xabmfl90KBBMnz4cNMiVCtSCQgggICdgP4NOffccxk32A6K/Qj8n4B+J3fq1Mn8OAUFAQTsBbQiR8sMlTn2VsRAQAV0Lge9N9N7NAICCNgLaA9ZXQgIIBAagYAqQHWmv9jY2AJTrDeOX375pcyePduMBZo7YsWKFeWZZ54xEyVpS1EdG1RvNhctWpQ7Gq8RQAABBBBAAAEEEEAAAQQQQAABBBBAAIFCCwRUAdq5c2fT9V3HAC0oREdHy8iRI2XUqFEeo2iXiUmTJpmKz1KlSsmFF17oMR4bEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBQAUCqgC94IILzERGrVu3NhMcFXRxbd69c+dO2bt3b0FRpE2bNvLjjz/K1KlTC4zDDgQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBCBgCpAa9euLWPHjjWVoL169ZLevXuLjguaN6xZs0bS0tJk69ateXfle3/xxRfn28YGBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgMAIBj1g9YMAA+eOPP+Tll182433qmJ8tW7aUc845x0xutHv3bvn444/NWKHNmzcvTBo5FgEEEDACx44dMwPt67AZBAQQsBfIzMyUmJgYiYiIsI9MDARcLnDq1Ck5evSolC1b1uUSZB8B3wROnDghOjeEDn1GQAABe4GsrCwTKSoqyj4yMRBAwHGBgCtANSUvvfSSnH322XLfffeZbu7Lly8XXXKHt956iy/F3CC8RgCBgAS0ImfmzJlSq1Ytad++fUDn4CAE3CSgPTAWLFggzZo1k6ZNm7op6+QVgYAE1q9fL2vXrhWdpFO/awgIIOBdYPHixaa3X/fu3Xlw4J2KvQgYgTlz5ph1cnIyIgggEAKBgLrA507nP/7xD3Oz+OCDD5rZ3HUGeF20Jei3334rAwcOzB2d1wgggEBAAtr68+TJk3LkyJGAjucgBNwmoA8NNFBm3PbJk99ABawyY60DPQ/HIeAWAf1+0XszvUcjIICAvYB+v/AdY+9EDASCJVCoFqBWoqpVqybPPvuseatfgpGRha5XtU7NGgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCFjAkQrQ3Fen8jO3Bq8RQMApAetvi7V26rycB4FwFbDKirUO13ySLwScErDKirV26rycB4FwFbDKirUO13ySLwScEqCsOCXJeRAITMDxCtDAksFRCCCAgHeB2NhYSUpKkkqVKnmPyF4EEDACcXFxooPt65qAAAL2AomJiVK+fHmpW7eufWRiIICAtGjRQjIyMsykt3AggIC9APMY2BsRA4FgClABGkxdzo0AAo4KJCQkOHo+ToZAOAuULl1aGjduHM5ZJG8IOCqgs79TZhwl5WRhLlC5cmXRhYAAAr4JMMGeb07EQiBYAn5XgC5atEhGjRolgwYNMpMeeUvY4cOH5aeffhKdIVBnh69evbq0bt06Z4mIiPB2OPsQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFCCfhdAdquXTtJS0uTzp07S58+feSTTz7xmIAff/xRbr75Ztm8ebPH/Zdccom88847wlMQjzxsRAABBBBAAAEEEEAAAQQQQAABBBBAAAEHBPyuANVrWl0dVq9e7TEJjz/+uIwYMUJOnTqVs1/HiGnWrJmUK1dO1q1bJ/PmzTPjxrz//vvSvXv3nHi8QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEnBIIqALUunjuCk5r26+//npa5ad2eX/ttdekY8eOVhSz3rdvn2hF6fXXXy9akVqjRo3T9vMGAQQQQAABBBBAAAEEEEAAAQQQQAABBBAorEBkYU+Q+/iTJ0/K0KFDc1p+tm3bVmbPnp2v8lOPqVatmowePVqGDx8u9957b+7T8BoBBBDwKKBDauzfv9/jPjYigMDpAtnZ2ZKamiqZmZmn7+AdAgh4FNCyomVGyw4BAQTsBfSerKDhzuyPJgYC7hPYtWuX6EJAAIHQCDhaAfrWW2/JkiVLTE6io6Nl6tSpUqlSJa85u+eee2Tbtm3y+++/e43HTgQQcLfAgQMHzGRqK1ascDcEuUfAR4EdO3aYHhYpKSk+HkE0BNwtoGVFeyVp2SEggIC9gN6T6US3eo9GQAABe4GFCxeKLgQEEAiNgKMVoBMnTszJhbYEbdCgQc57by+Sk5Nl/vz53qKwDwEEXC6gLcw1WGuXc5B9BGwFrLJirW0PIAICLhewyoq1djkH2UfAVsAqK9ba9gAiIOByAR1C0NMwgi5nIfsIFJmAYxWg2m1o0aJFOQkfNGhQzmu7F5GRkeaJu1089iOAAAIIIIAAAggggAACCCCAAAIIIIAAAv4IBFQB2q1bNzMT/J9//imvvPKKHD58WLQLhDVm0llnnSWJiYk+pePYsWMybtw4KkB90iISAu4V0GE19GFJuXLl3ItAzhHwQ8AqK9baj0OJioArBayyYq1diUCmEfBDQMuK3pvpPRoBAQTsBcqWLSu6EBBAIDQCAc0CP3jwYLnhhhtEx/x89dVX5dlnnxWd7d0KXbt2tV7arl966SXZuHGj1KxZ0zYuERBAwL0CerOgw2VERUW5F4GcI+CHgH6v9ujRgx+mfpgR1d0C+vA+Pj5eYmJi3A1B7hHwUUAnvM3KyuJ7xkcvoiHgTz0JWggg4LxAQC1ANRkVK1YUncBow4YNphJ09+7dOanTm0dfwr59+0zlqcZNSEjw5RDiIICAiwWsVqAuJiDrCPgloBU5ERERfh1DZATcKqBlhcpPt3765DsQAVp/BqLGMW4W0IYcNOZw8/8A8h5qgYArQK2Ely5d2rQGXbp0qcyYMcO0NmnUqJG12+taZ0DT7vMacrcg9XoQOxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8FAuoCX9C5u3fvLrr4Gtq3by9nnnmmaf2ps8YTEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABJwUcrQD1N2HVqlWTNWvW+HsY8RFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZ8ECt0F3qerEAkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiBgGMtQHUM0N9//11SUlLMomOD1q5dW5o2bSoXXXSR6eoegvxxSQQQCBOB7Oxs0XGDdWbrJk2ahEmuyAYCwRM4dOiQLFmyRM444wypVatW8C7EmREIE4G0tDRZt26dGZe+QoUKYZIrsoFA8ARSU1NFJ8LVYc30tx8BAQS8C2idiYZWrVp5j8heBBAIikChv6nmzp0rI0eOlJkzZ3pNYMOGDeXOO++UW265RcqXL+81LjsRQACBvAJambNnzx45fvw4FaB5cXiPgAeBvXv3Snp6uuzcuZMKUA8+bEIgr4CWFS0zWnaoAM2rw3sE8gts27ZNMjIyRO/RKleunD8CWxBA4DSBrVu3mvdUgJ7GwhsEikwg4C7wp06dkoEDB0qXLl1sKz81N5s2bZJ77rnHTHj0/vvvF1kGuRACCCCAAAIIIIAAAggggAACCCCAAAIIuFcg4BagDz74oEyYMMHI1atXT7p16yZ16tQx3d4rVqwoR48eNU8ER48eLTrZ0fr16+XYsWOyb98+6devn8yYMUPGjRsn0dHR7tUn5wgggAACCCCAAAIIIIAAAggggAACCCAQVIGAKkCnTZsmL7zwgpx33nnyzDPPSKdOnSQy0nNjUo3zyCOPmC54H3zwgYwZM0b++OMPeffdd02FqG6LiIgIaiY5OQIIlHwB7Y6o43/qQkAAAXuB6tWrS9WqVSUuLs4+MjEQQMCUlYMHD4qWHQICCNgL1K9f3zRmYcgIeytiIKACCQkJQCCAQAgFAqoA/f7776V3797y0UcfSUxMjNfkX3DBBabyUwfJvv32280YoPfee69oy1A9vlGjRvL00097PQc7EUAAAR1c/9xzzwUCAQR8FNAfpPqAkoAAAr4J8JDNNydiIWAJNG7cWHQhIICAbwJJSUm+RSQWAggERcBzs02bS82fP9+0/LSr/LROo7PAjxo1yrwtU6aMvP766zJ16lSJjY0159FZagkIIIAAAggggAACCCCAAAIIIIAAAggggIDTAn5XgJ48edLMkPm3v/3N57RERUWJVprmDn379hWt+NTxQUeMGJF7F68RQAABBBBAAAEEEEAAAQQQQAABBBBAAAFHBPyuANWxPnVspP379/ucgB9//FEOHz6cL752mXj22Wfliy++kFWrVuXbzwYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAwAn5XgOrFWrduLffdd5+cOnXK67W1tajGW7lypbRs2dJj3H/+85/Srl07mTJlisf9bEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBAIVCKgCdMiQITJp0iTp0qWLGcszLS1NtLJTQ1ZWlmzYsMGM86kzwL/88stme8eOHc067z86A/yrr74q06dPz7uL9wgggMBpAseOHZMTJ06cto03CCBQsEBmZqbtw8qCj2YPAu4S0Af7WmYICCDgm4Dek+m9GQEBBHwT0LoSXQgIIBAagYBmgW/fvr2p2Lzjjjvkp59+MinXGZorVqxousbnbRmqrT/vv//+AnOoMzunp6ebio1SpUoVGI8dCCDgXgH9UTpz5kypVauW6N8gAgIIeBfQh5MLFiyQZs2aSdOmTb1HZi8CCMj69etl7dq10qFDB/NdAwkCCHgXWLx4seh3Tffu3aVs2bLeI7MXAQRkzpw5RiE5ORkNBBAIgUBALUA1nUOHDpUxY8aY8UD1fXZ2tqnE9FT5OW3aNNsvxW7duklqaqqeioAAAgjkE9AWBtrS/MiRI/n2sQEBBPILWC3ZKDP5bdiCgCcBq8xYa09x2IYAAv8T0O8XvTejFej/THiFgDcB/X7hO8abEPsQCK5AwBWgmqzbbrtNUlJS5NFHH5WLLrpI4uPjRbu016lTRy688EJ56623zEzvOtmRXdCZ4Bs2bGgXjf0IIIAAAggggAACCCCAAAIIIIAAAggggIDPAgF1gc999sqVK8tTTz2Vs0nHggmkG7vOLE9AAAEEChKIjPzv8xprXVA8tiOAwH8FrLJirXFBAAHvAlZZsdbeY7MXAQSssmKtEUEAAe8ClBXvPuxFINgCha4AzZvAQCo/856D9wgggEBegdjYWElKSpJKlSrl3cV7BBDwIBAXF2cG2tc1AQEE7AUSExOlfPnyUrduXfvIxEAAAWnRooVkZGSI3qMREEDAXoB5DOyNiIFAMAUcrwANZmI5NwIIuFsgISHB3QDkHgE/BHRyQl+GoPHjlERFIKwFdBIXykxYf8RkzmEB7QmoCwEBBHwT0MlcCQggEDqBQo0BGrpkc2UEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBegApQeyNiIIAAAggggAACCCCAAAIIIIAAAggggEAJFaACtIR+cCQbAQQQQAABBBBAAAEEEEAAAQQQQAABBOwFqAC1NyIGAggggAACCCCAAAIIIIAAAggggAACCJRQASpAS+gHR7IRcKPA5s2bZf/+/W7MOnlGwG+B7OxsSU1NlczMTL+P5QAE3CigZUXLjJYdAgII2AvoPZnemxEQQMA3gV27dokuBAQQCI0AFaChceeqCCDgp8CBAwdk+fLlsmLFCj+PJDoC7hTYsWOHrF69WlJSUtwJQK4R8FNAy4qWGS07BAQQsBfQezK9N9N7NAICCNgLLFy4UHQhIIBAaASKRQXoiRMn5Pvvvw+NAFdFAIESIXDy5EmTTmtdIhJNIhEIoYBVVqx1CJPCpREoEQJWWbHWJSLRJBKBEApYZcVahzApXBqBEiFw6tQp0YWAAAKhESgWFaAzZsyQBQsWhEaAqyKAAAIIIIAAAggggAACCCCAAAIIIIBA2AqUDkbOsrKyZPHixbJq1SrTlSg9Pd3jZTTe7t275aeffpLhw4d7jMNGBBBAQAWio6MlMjJSypUrBwgCCPggYJUVa+3DIURBwNUCVlmx1q7GIPMI+CCgZeXgwYPmHs2H6ERBwPUCZcuWdb0BAAiEUsDxCtB9+/ZJnz59ZO7cuaHMF9dGAIEwE9AbhuTkZImKigqznJEdBIIjULNmTenRowc/TIPDy1nDUCAxMVHi4+MlJiYmDHNHlhBwXqBt27aiDVr0ITUBAQTsBbp27WofiRgIIBA0AccrQK+77joqP4P2cXFiBNwtwA22uz9/cu+/ABU5/ptxhHsFIiIiqPx078dPzgMQ0J453JsFAMchrhWgIYdrP3oyXkwEHK0APXbsmMybN89krV+/ftKzZ09p166daCsUTyE7O1vS0tLkmWee8bSbbQgggAACCCCAAAIIIIAAAggggAACCCCAQKEEHK0A3bx5sxw9elQ6d+4skyZN8ilhlStXlueff17GjRvnU3wiIYAAAggggAACCCCAAAIIIIAAAggggAACvgo4Ogt8QkKCmaTk7LPP9vX6Jl7t2rXltttu8+sYIiOAAAIIIIAAAggggAACCCCAAAIIIIAAAnYCjlaA6lhjV111laxZs8buuvn26xgyBAQQQAABBBBAAAEEEEAAAQQQQAABBBBAwEkBx2sdn376aVm1apXMnz/f53TqOKBvvPGGz/GJiAAC7hPQMYP170pqaqr7Mk+OEQhA4NChQ/LTTz+ZsbYDOJxDEHCdgN6PapnRskNAAAF7Ab0n03szvUcjIICAvcDSpUtFFwICCIRGwPEK0MaNG8sPP/xgurRv2LDBjAmq44J6Wg4ePCga58EHH5RTp06FRoCrIoBAiRDQH6R79uyRbdu2lYj0kkgEQi2wd+9eSU9Pl507d4Y6KVwfgRIhoGVFy4yWHQICCNgL6D2Z3pvx0MDeihgIqMDWrVvNggYCCIRGwO9JkLR7u87sbhd0RvgmTZrYRcvZ//jjj+e85gUCCCCAAAIIIIAAAggggAACCCCAAAIIIOCEgN8VoM2aNZOaNWvKpk2bnLg+50AAAQQQQAABBBBAAAEEEEAAAQQQQAABBIIm4HcFqKakZ8+eZszODh06SIMGDSQiIiKgBGZlZcnu3bvll19+Ceh4Nx105MgRKVeunJuyTF4ROE2gQoUK5uGLPoAhIICAvUD16tWlatWqEhcXZx+ZGAggYMqKDs+kZYeAAAL2AvXr15fo6GjRezQCAgjYCyQkJNhHIgYCCARNIKAK0Msuu0zWrVtnxvp0ImVffvmlLFmyxIlThe05dGIpnWCqW7ducuONN0qVKlXCNq9kDAFPAqVLl5Zzzz3X0y62IYCABwH9QdqpUycPe9iEAAKeBPQBGw/ZPMmwDQHPAjr3gy4EBBDwTSApKcm3iMRCAIGgCARUAdqlSxfZvn27Ywm65JJL5PDhw46drzifSFu9LliwQJYvXy4ZGRnSvHlzadWqldg9DWrfvr1MmDBB2rZtKw888IBcddVV8t577xXnrJI2BBBAAAEEEEAAAQQQQAABBBBAAAEEQi4Q0Czw2tVhwIABHhO/efNm0QmQfA133nmnZGZmynXXXefrISU2nrZy1QrMzp07yx133CGPPPKIXHHFFWYYgWuuuUY2bNjgNW/VqlWTPn36yNGjR2XSpEle47ITAQQQQAABBBBAAAEEEEAAAQQQQAABBEQCqgD1BqddVJcuXeotymn7tDu3VgIeP378tO3h9kZNdMzUFStWeMzaxx9/LDrB1PPPP+9xv7Wxdu3a1kvWCCCAAAIIIIAAAggggAACCCCAAAIIIGAjEFAXeJtz+rW7TZs2ZhKksWPHirYGDceg3d5vvvlm0bWGqKgoGThwoGkNWq9ePUlJSTGVxj/88IM8+OCDpnv8O++8YwYVz+uhxxIQQAABBBBAAAEEEEAAAQQQQAABBBBAwDeBkFaApqenm9nktcv8rFmzwrYC1BrzUz+SOnXqyPTp0+Wss87K+YR69OhhXutQAC+//LJpBZqcnCyff/45kx3lKPECATHDa+hkSKVKlYIDAQR8ENDvlZiYGImIiPAhNlEQcLfAqVOnzDBDZcuWdTcEuUfAR4ETJ05Idna2x0YbPp6CaAi4SiB3gyhXZZzMIlBMBArdBV7HrtQKCWvZtWuXXHjhhRIbG2uWSpUqSeXKlU1FXtWqVUXHsaxevbro7LT6/tFHHzUUGi9cg87gboU333zztMpPa7uu9YZbxwXVFqH6g1XHCt25c2fuKLxGwLUCWpGjDw8WL17sWgMyjoA/AmlpaabM6HcKAQEE7AXWr19vyoyWHQICCNgL6D2Z3pvpPRoBAQTsBebMmSO6EBBAIDQChW4BOmXKFBk1apT861//yuni7e+XYPny5QucVCk0LM5edfv27eaEWincq1cv25PrOJ/fffedDB8+XM4//3xzY9G4cWPb44iAQDgLaEvxkydPypEjR8I5m+QNAccErO9iyoxjpJwozAWsMmOtwzy7xTJ72gpXH/7r970VrAppnS9g27Zt1maz1gYV2tCCEBoB/X7Rz0rv0Wg5HZrPgKuWLAG+X0rW50Vqw0+g0BWgSnLXXXeZ8Sx79uwp+/fvl1atWpnWn964IiMjpX79+pKYmGhmNtcJgMI1NG3a1GStYcOGPndDVB+dEEmP7dq1q6kQbd68ebgSkS8EEEAAAQQQQAABlwvofACjR4/2qLBy5Urz2yH3Tu2BtnDhQvPbI/d2XiOAAAIIIIAAAnkFHKkA1ZN27NhRvv76a9GxK8eMGWNmPM97Mbe+19nftUJTuyHqWDn+jF+okyXpRElaufzpp5+6lZB8I2DKkDJoWSIggIC9gFVWrLX9EcRAwN0CVlmx1u7WCE3uzzjjDImPjz+tBai2MNy7d68ZHkp7U+UO2vpTh9QihEbAKivWOjSp4KoIlBwBykrJ+axIaXgKOFYBqjxaCfrFF1+YiX785dLuLjpBUDgGbcV5ww03yKRJk8xkT1pJ7E/QSZJ0qIGrr75a2rVr58+hxEUgbAT0R09SUpKE83jBYfNhkZFiIRAXF2eGptE1AQEE7AW0V5IOy1S3bl37yMQIisDQoUNFF0LJEGjRooVkZGTY9vwrGbkhlQgEX6B9+/bBvwhXQACBAgUcb0rVvXt3SUhIKPCCnnboF+f48eM97Qqbbc8++6zpzt6/f3/RiaL8DfrH8quvvpJ58+b5eyjxEQgbAf3bwlhfYfNxkpEgC2jXUB0/mnHZggzN6cNGQMuKlhl/euqETebJCAIBCOg9mb+/+wK4DIcgEDYCtWrVEl0ICCAQGgHHK0D9zYYOdq6Vn7oO56CtCX777Tdp2bKlacU5bdq007r3+JL3M88801SAMiGSL1rEQQABBBBAAAEEEEAAAQQQQAABBBBAQCSgLvCPPfaY/Pjjj/Loo4/KRRddlOOoEyBpN3gd59KXkJWVZcb0OXDggDz++OO+HFKi4+hTUp3dfdGiRfLhhx/K5MmT5bPPPvMrTw0aNDCVoNrSloAAAggggAACCCCAAAIIIIAAAggggAAC3gX8rgBdsWKFjBgxwpz13nvvFX1vBa3g07HGZs6caW1i7UFAx/EszFieOlbq8uXLPZyZTQgggAACCCCAAAIIIIAAAggggAACCCCQW8DvCtCGDRuaSUh03E6dkCRvuOmmm0wFqI6jpJWhMTExeaPkvNcWoHv27JH09PScbbzwTUDHdiMggAACCCCAAAIIIIAAAggggAACCCCAgHcBv2vRKlasKBs3bpRly5bJBRdckO/svXv3lpo1a8qSJUt8nkXzxRdflMOHD+c7FxvyC/z73/+WUaNGycUXXyxjx47NH4EtCISxwObNm80kSEyEFMYfMllzTCA7O1u0zOgY1EyE5BgrJwpjgczMTNm+fbvocEM8aA7jD5qsOSagw5/pomWGgAAC9gLWZMi1a9e2j0wMBBBwXCCgSZCqVq0qF154ocdZMmNjY0VnOtcfXL6Gm2++2deoro6nNxgPPvigbNmyRcaNGyc///yzqz3IvLsEdKxgHfoh97Ab7hIgtwj4J7Bjxw5ZvXq1pKSk+HcgsRFwqYCWFS0zWnYICCBgL6D3ZHpvpvdoBAQQsBdYuHCh6EJAAIHQCARUAWqX1DvuuMMuymn7q1evLn379j1tG2/yC2jlslWxHBUVJcwGn9+ILeErcPLkSZM5ax2+OSVnCDgjYJUVa+3MWTkLAuErYJUVax2+OSVnCDgjYJUVa+3MWTkLAuErcOrUKdGFgAACoRHwuwu8L8msV6+eL9FOi3PWWWed9p43+QUiIyNl7ty5MmXKFNFZ4HUyJAICCCCAAAIIIIAAAggggAACCCCAAAIIFCzgeAXotm3bJJAK0IKTyJ7cAvXr15f77rsv9yZeI+AKgejoaNGHAOXKlXNFfskkAoUVsMqKtS7s+TgegXAXsMqKtQ73/JI/BAoroGXl4MGDovdoBAQQsBdgTHZ7I2IgEEwBRytAT5w4IV27dpWPPvpI2rRpE8x0c24EEHCZgN4wJCcniw7/QEAAAXsBnZCwR48e/DC1pyIGAkYgMTFR4uPjJSYmBhEEEPBBoG3btpKVlcX3jA9WREFABbSuhIAAAqETcLQCVLORmpoq+mV4/vnny2233SZ9+vThSzF0n69jVz58+LA4Mb6PVpJr0HPpDZPOshoREVFgOjWOBo3jbUZWPZ91bm0lWKpUqQLPqfGsvHD9kuVfUAsDPv/Q/v+3xjLSWce1fFH+is/fn7wVOfz9Kz5//wv8kvq/HXz/Ff33v95rWGUG/6L3z10m8C8Z/vqZ6X23fl7cf/P7g/s/3+//cv+9y/uav38l4+9fqH9/5v1/w3vfBByvANVJeh544AHZtGmTDB8+XO666y4zK/ygQYOkSZMmvqWKWMVK4M4775TXX3/d0TStW7dOvv32W/NDQ8cz9fSFuWDBAklLS8u5boMGDaRly5Y5760XR48elVmzZolWvmjQHzAdOnQQbf2UN2zdulWWLl2as1l/6HB9/Pn/l/+GzZ/yZ83++vPPP5syS/nj7w9/f+2/f/SLqKAHBv6UP77/+P7n/of7P/17wv0v9//cf3H/xf2X/f1XOPz+17/5hMAEHK0A1S/eRx55RAYPHmxS8/LLL8sPP/wgb775pjRr1ky6dOli9vXu3dtra77AslLyjjpy5IipBNRWszp26vbt20UrEqpVqybVq1cXrfBTMx1OwFMFTVHlWNOiM9BbrbwKc12rJam2vNRF/wDp/xtPQVv75W71WVDrP33inHufWuU+Lve5dbt2obbywvXx5/9f4cufZajlq0yZMpS//+/gKfD3h7+/ub9/9P+IVXby/n/h+4/v/9z3MbnvcXL/X+H+h/u/3P83uP/l/j/3343cfyu4/+D+I/f9B79/S/7v39zlm9f+CUT8/4qgU/4dEljsP//8U8aPHy9vvfWWOcHAgQPllltukYSEhMBOWIKPmj17tkycOFE+++wzOXTokG1OtPKxb9++pmVt06ZNbeMX5witW7c2LTD//ve/m/wX57SSNgQQ8E1Ahz1ZvHix/P7774z/7BsZsVwuYFV8FtEtmMu1yT4CCCDgLgH9za29L2+99VYZN26cuzJPbhEIU4Ebb7xRJk+eLJMmTZJ+/fqFaS6Dn63I4F/iv1fQ2ctHjBgh2gV51KhRpgKwUaNG0rNnT/n6669zxmQsqvSE4jq7d+82Y6J269bN/Mf1pfJT06mtQidMmGBa0Q4ZMiSnq3co8sA1EUAAAQQQQAABBBBAAAEEEEAAAQQQKEkCnvvpBSkH2sX73XfflXfeecd099bLfPPNNzJjxgx5/PHH5aGHHgrSlUN/2r1790rnzp1Fx77UoLOMnn322aIzjmrlcLly5cyiTdIzMzPl4MGDZtHKTz1m4cKFxkyHE9iwYYN89dVXpqtp6HNGChAoGgEd40zLgY5tw3jCRWPOVUq2gD5kW7JkiZxxxhlSq1atkp0ZUo9AEQjouON6z6W9VSpUqFAEV+QSCJRsAR3GSxt4tG/fvsDhb0p2Dkk9As4KWHNRtGrVytkTczYEEPBJwNEKUB3Mf+TIkaYy07r68ePHTWXd22+/LdOnT8+ZqVv3N27c2HSDHzBggNSoUcM6JCzX2uVfbxKGDh1q8tyiRQu/87ljxw557rnn5I033pDRo0fLsGHD/D4HByBQUgW0MmfPnj2if1OoAC2pnyLpLkoBffCWnp4uO3fupAK0KOG5VokV0LKiZUbLDhWgJfZjJOFFKKBzGGRkZJghvSpXrlyEV+ZSCJRMAe0Nq4EK0JL5+ZHqki/gaAWocnz44Ydyww03yL59++Sjjz4y4xTojaQVdBDmXr16mXFJLrroogInALDih8N6zZo1ZjKoOXPmSKdOnQLOUlxcnLz22mtm2IBrr71W7r77btEB8AkIIIAAAggggAACCCCAAAIIIIAAAggg4FnA8QpQ7Tqk3brzBu3yrZMeaUvIOnXq5N0d1u9/++03U+FbmMrP3EDJyclmlvhNmzaZVrS59/EaAQQQQAABBBBAAAEEEEAAAQQQQAABBP4nEPTmg1Zlp7b21FaL1vv/JSH8X+nYOM2bN3c0o+ecc45otxMCAm4R0O6IOv6njplLQAABe4Hq1atL1apVRXsPEBBAwF5Ay4qWGS07BAQQsBfQezK9N2PICHsrYiCgAgkJCWZBAwEEQiMQlArQatWqyWOPPWZmfNdxK7Xrt1bWnXnmmaYSdNmyZaHJbYiuWq9ePVm8eLFjVz916pSoYcuWLR07JydCoLgL6PAZ5557Lq2ei/sHRfqKjYD+INWeB/rjlIAAAvYCWla0zFCZY29FDARUQOdz0HszvUcjIICAvUBSUpLoQkAAgdAIOF4BqpOTrFy5Up588smcllpdunSR77//Xn7//XczZmW7du3k0ksvlXnz5oUm10V8VW2tqTPff/75545cWW2joqKEwcYd4eQkCCCAAAIIIIAAAggggAACCCCAAAJhLOBoBWhERISZAb6gbu76tOODDz4QHSdUZ3Pu3LmznH/++fLNN9+EMfF/n47qxE9XXHGFXH311TJr1izJzs72K88nTpyQGTNmSI8ePUzl8q233urX8URGAAEEEEAAAQQQQAABBBBAAAEEEEDAjQKO9lfQGcn79etXoKPODD9x4kQZP368rF+/3sT75Zdf5PLLLzcVp48//niBx5b0HWPHjpVVq1bJ1KlTzVKpUiUzLqi2mG3QoIGUL19eypUrJ2XLljWVo4cPH5YjR47I9u3bRWeR11a1f/31l2F4+OGHZfDgwSWdhPQjgAACCCCAAAIIIIAAAggggAACCCAQdAFHK0B1bMpff/1VzjvvvJyE67Yff/xR3nrrLZk2bZocO3YsZ5+OFXrzzTeLtmb0NHN8TsQweKED6utYqIMGDZKvvvpKMjIyZP78+WbxNXtaOTps2DAZOXKkr4cQDwEEEEAAAQQQQAABBBBAAAEEEEAAAVcLOFoBevLkSdHWiV9++aVs2bJFPvnkE5k0aZJs3LjxNGTt9q4tGPv27SvR0dGn7QvnNzo0gNroeKjqohWhBw8etM1yjRo15Pbbb5chQ4aIviYg4FYBfYCiA+2XKlXKrQTkGwG/BDIzMyUmJkZ0iBoCAgh4F9CH9kePHjW9cbzHZC8CCKiADtGlw3q56fccnzwChRHIysoyh+t8HgQEECh6AUcrQDX52tpTJ+fRytDcQbt833jjjabis3nz5rl3ue71xRdfLLroTfbMmTMlJSVFdu7cKbt27ZIDBw6IVpTGx8ebpX79+qKTRmnrTwICbhbQihwtL7Vq1ZL27du7mYK8I+CTQFpamixYsECaNWsmTZs29ekYIiHgZgEdnmnt2rXSoUMH813jZgvyjoAvAosXLxb9runevTu/VXwBI47rBbRHqIbk5GTXWwCAQCgEHK8A1UzkrvzUyjtt7XnttdeaMS5Dkcniek1tldOzZ8/imjzShUCxEtDWn/q3RcfGJSCAgL2APjTQQJmxtyIGAipglRlrjQoCCHgX0O8XvTfTezQaa3i3Yi8CKsD3C/8PEAitQFAqQPUL8LrrrjPdtlu3bh3aHJbQqx8/flx2795tupTQ7b2EfogkGwEEEEAAAQQQQAABBBBAAAEEEEAg5AKOV4BqN7u5c+dK7dq1Q565kpyApUuXmi5Y55xzjunCWJLzQtoRcEIgMjLSnMZaO3FOzoFAOAtYZcVah3NeyRsCTghYZcVaO3FOzoFAOAtYZcVah3NeyRsCTghQVpxQ5BwIBC7gaAWoFugpU6ZQ+Rn458GRCCBQgEBsbKwkJSWJjidMQAABe4G4uDjRwfZ1TUAAAXuBxMREKV++vNStW9c+MjEQQEBatGghGRkZovdoBAQQsBdgHgN7I2IgEEwBRytAdZZZraAgIIAAAsEQSEhICMZpOScCYSlQunRpady4cVjmjUwhEAwBHcKJMhMMWc4ZrgI68a0uBAQQ8E1AJ3MlIIBA6AT+26c0dNfnyggggAACCCCAAAIIIIAAAggggAACCCCAQNAEqAANGi0nRgABBBBAAAEEEEAAAQQQQAABBBBAAIFQC1ABGupPgOsjgAACCCCAAAIIIIAAAggggAACCCCAQNAEqAANGi0nRgABBBBAAAEEEEAAAQQQQAABBBBAAIFQC1ABGupPoIDr62yKXbp0kTZt2hQQg80IuE9g8+bNsn//fvdlnBwjEIBAdna2pKamSmZmZgBHcwgC7hPQsqJlRssOAQEE7AX0nkzvzQgIIOCbwK5du0QXAgIIhEbA0VngQ5OF8Lxqs2bNZM6cOeGZOXKFQAACBw4ckOXLl0uVKlWkc+fOAZyBQxBwl8COHTtk9erVcuTIEWnRooW7Mk9uEQhAICUlRTZt2iRlypSR+Pj4AM7AIQi4S2DFihWSnp4uVatWFW28QUAAAe8CCxcuNBF69erlPSJ7EUAgKAJUgAaFlZMigIDTAidPnjSntNZOn5/zIRBuAlZZsdbhlj/yg4DTAlZZsdZOn5/zIRBuAlZZsdbhlj/yg4DTAqdOnXL6lJwPAQT8EAhJF/hFixaJtuYiIIAAAggggAACCCCAAAIIIIAAAggggAACwRQIWgVoRkaGPP/883LDDTfI/ffff1oedIylVq1ayUsvvXTadt4ggAACBQlER0dLZGSklCtXrqAobEcAgVwCVlmx1rl28RIBBDwIWGXFWnuIwiYEEMgloGVF7830Ho2AAAL2AmXLlhVdCAggEBqBoHSBnzVrlvTv31+2bdtmctW7d+/Tcqfj93333Xdy2WWXydKlS2XChAl8cZ4mxBsEEMgroDcLycnJEhUVlXcX7/8fe/cB51SV9nH8GXoHUUC6SFNBqlQBQRERRbGhIiCICmsXXVHXlVUR1NeCDcWuYMe6IKggNkQQRFAUARGk9yogbd75n92bzWSSSTLJZDKT3/l8wr2599xyvuFObp57CgIIBBGoXLmynXbaaXy/BrFhEQLBBOrXr+/6/ixRokSw1SxDAIEAgRNOOMH279/P90yAC28RCCXQpUuXUKtYjgACCRCIewD0jz/+sF69etmuXbuyPf0GDRrYF198YfXq1bMqVarYww8/nG1+ViKAAALUMOD/AALRCRDIic6L3KktkJaWZlwzqf1/gNJHJ0Dtz+i8yI0AFTn4P4BA3grEvQn80KFDXfBTTSJUW6t3794hS1itWjXXRP7RRx81b0S0kJlZgQACCCCAAAIIIIAAAggggAACCCCAAAIIRCkQ9wDo1KlTrUWLFq75+8cff2x9+vTJ9pQaN25sGjnwtddeyzYfKxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgWgF4hoAXbNmjWnwoyeeeMIOO+ywiM5l69atLt+CBQsiyk8mBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgUoG4BkA14IIGKmnSpEmkx7dvvvnG5d22bVvE25ARAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIRCCuAdAiRYpY8+bN7aeffork2Pb+++/bp59+6vJqFEESAgggEErgwIED7oHJ0qVLQ2VhOQII+AloMMIvv/zS1q9f77eUWQQQCCWga0XXTLiBPENtz3IEUk1A92SqzKJ7NBICCIQXmDdvnulFQgCBvBGIawBURTj//PPtX//6l+3duzfbEn3wwQc2YMAAX562bdv65plBAAEEAgX0g3Tjxo2uf+HAdbxHAIGsAps2bTJ1M7N27dqsK1mCAAJZBHSt6JrRtUNCAIHwAqtWrXL3Zjw0CG9FDgQk8Mcff7gXGgggkDcCReJ92BtuuMG++uor69Spkw0ZMsTS0tLcIXbu3Gm//vqr/fLLL/b000/7mr5rZdeuXa1///7xPhX2hwACCCCAAAIIIIAAAggggAACCCCAAAIpLhD3AKgCni+//LJdcMEFNmjQIMerpnDdj8oAAEAASURBVPHlypULSq0R4ydMmGDKQ0IAAQQQQAABBBBAAAEEEEAAAQQQQAABBOIpEPcm8Dq5smXL2pQpU2zy5MnWtGnToP3CNGzY0MaPH2/fffedlS9fPp5lYl8IIFAABcqUKWMaaK1mzZoFsHQUCYH4CxxxxBFWsWJFq1atWvx3zh4RKIACulZ0zejaISGAQHgB3ZPp3kz3aCQEEAgvULt2bdOLhAACeSOQq9Uuu3fvbnqp+fuiRYts+fLlVrVqVWvQoIH7ssybInNUBBDIjwKqJd6uXbv8eOqcMwJ5IqAfpB07dsyTY3NQBPKjgAI5epEQQCAygbp165peJAQQiEygWbNmkWUkFwII5IpA3GuAKsj5119/ZTpZ1Qht1aqVaxbfoUOHTDeX1113ne3YsSNTft4ggAACCCCAAAIIIIAAAggggAACCCCAAALxEIh7AFQ1tObNmxfxufXr18/OOecc27dvX8TbkBEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhEIO4B0EgO6p+nZcuWNmPGDDcyvP9y5hFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVgF8jQAunXrVhs5cqRrMj9t2rRYy8L2CCCAAAIIIIAAAggggAACCCCAAAIIIIBAJoGYB0G68MIL7Z133vHt9ODBg3byySebBixRSktLC/rau3ev/fnnn77tGAneR8EMAgiEEFD/wvrbUrhw4RA5WIwAAv4Ce/bssRIlSrjvYf/lzCOAQFaB9PR00/1pyZIls65kCQIIZBHQ774DBw5Y8eLFs6xjAQIIZBXYv3+/W1i0aNGsK1mCAAK5LhBzDdA333zTHnroIStUqJDpS1BJP7g08rteGuBo+/bttm3bNlONzy1bttjmzZszBT9Lly5tl112Wa4XlgMggED+FdDflU8++cTmzp2bfwvBmSOQQIH169e7a2bJkiUJPCqHQiD/CixevNhdM7p2SAggEF5A92S6N9M9GgkBBMILTJ8+3fQiIYBA3gjEXANUp3399dfbCSecYGeeeaYLdDZv3tzKlSuXbYkUMK1Zs6bVr1/fzjvvPDv22GOzzc9KBBBIbQHV/jx06JDt3r07tSEoPQIRCng/SLlmIgQjW8oLeNeMN015EAAQCCOg7xfdm+kejZrTYbBYjUCGAN8v/DdAIG8F4hIAVRFOPPFEmzhxonXr1s3GjBljbdu2zduScXQEEEAAAQQQQAABBBBAAAEEEEAAAQQQSHmBmJvA+wsqCPrBBx9Y1apV/RdnO79u3Trz+sLINiMrEUAgpQVUa1zJm6Y0BoVHIAIB71rxphFsQhYEUlrAu1a8aUpjUHgEIhDwrhVvGsEmZEEgpQV0rXC9pPR/AQqfxwJxqwHqlaNr167ebETTGTNm2Ouvv25vvfUWfwwiEiMTAqkpoG41mjVrZgyYlpqfP6WOXqBatWruAaOmJAQQCC+gbpnUL3316tXDZyYHAghYkyZN3FgP4bo+gwoBBP4j0Lp1aygQQCAPBeJaAzQn5ShTpowbRf7TTz/NyeZsgwACKSRQu3Ztq1ChQgqVmKIikHOBIkWKWN26demXLeeEbJliAurDUNdM4cKFU6zkFBeBnAnonkz3ZiQEEIhMoEqVKqYXCQEE8kYg7jVAVYy1a9fauHHj3GjNmzZtch1jByven3/+ad7otB9++KGddtppwbKxDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyJFA3AOgK1eutI4dO9qKFSuiOqGTTjopqvxkRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEwgnEPQB63nnnueBnpUqVrGLFiqYmeMuWLbOjjz4607mkp6e72p/HHXecXXnllabtSAgggAACCCCAAAIIIIAAAggggAACCCCAQDwF4hoAVZP2xYsX2+eff27+NTovuugiu/vuu61BgwaZzl21RS+99FLr168f/S1lkuENAggggAACCCCAAAIIIIAAAggggAACCMRDIK6DIM2fP9969eqVKfipk7ziiivs0UcfzXK+NWvWtHbt2tngwYOzrGMBAgggECiwfPly27ZtW+Bi3iOAQBCBAwcO2NKlS23Pnj1B1rIIAQQCBXSt6JrRtUNCAIHwAron070ZCQEEIhNYt26d6UVCAIG8EYhrAPTQoUNBRzU7+eSTbcaMGbZ58+YspezWrZu9/vrrNmfOnCzrWIAAAgh4Ajt27DA9ZFmwYIG3iCkCCGQjsGbNGlu4cKFvsMFssrIKAQQyBDQwp64ZXTskBBAIL6B7Mt2b6R6NhAAC4QVmz55tepEQQCBvBOIaAK1Tp45r/h5YlLS0NBswYICdf/75tm/fvkyr1WReafz48ZmW8wYBBBDwF9ADFiVv6r+OeQQQyCrgXSveNGsOliCAgL+Ad614U/91zCOAQFYB71rxpllzsAQBBPwFNA6KXiQEEMgbgbgGQKtVq2bbt2+3Ll262L333uuave/atcuVTM3cf/31VzfY0TfffOMCoV9++aXdc889bj3NJ/LmPwBHRQABBBBAAAEEEEAAAQQQQAABBBBAoCALxHUQJNX0HDVqlJ177rm+mqAaAV79f5YsWdJGjhxpAwcOtIkTJ1qhQoUy1eRq2LBhQXambAggEKNA8eLF3d+NUqVKxbgnNkcgNQS8a8WbpkapKSUCORfwrhVvmvM9sSUCqSGga2Xnzp2mezQSAgiEF1BMhIQAAnknENcAqIpxzjnn2JNPPmk33nij7d+/3zp06OArnZrBqx/Qm2++OVPws1KlSjZ06FBfPmYQQACBQAHdMKjP4KJFiwau4j0CCAQRqFy5sp122mn8MA1iwyIEggnUr1/fatWqZSVKlAi2mmUIIBAgcMIJJ7jfewRAA2B4i0AIAbWUJSGAQN4JxD0AqqJcddVV1rdvX9chdo0aNTKV7qabbrLGjRvbhx9+aD/88IM1atTIhg8fHnTwpEwb8gYBBFJegBvsvP8vsGLFiiyDHXijjGv05GLFivlOUjX9GzRoQNDaJ5L4GQI5iTfniPlXQC2ZuGby7+fHmSdeQN/z3Jsl3p0j5l8BKnLk38+OMy8YArkSABVNuXLl3CsYk2qk6EVCAAEEEMg/ArNmzbJ27dqF7Lz9oosuylKYyy+/3J599tksy1mAAAIIIIAAAggggAACCCCAQKIEci0AGk0BVHvo1VdfNf1QJiGAAAIIJKdAzZo1rWPHjrZ169ZMJ7hp0ybTgHdqOqraIF7SvH83KN5ypggggAACCCCAAAIIIIAAAggkUiApAqCfffaZrVq1KpHl5lgIIIAAAlEKVKtWzb744osotyI7AggggAACCCCAAAIIIIAAAnkrkCsB0PT0dPv+++9NzSW3bNniRgc8ePBg0JKqz7iPP/7Yhg0bFnQ9CxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZwKxD0AumPHDuvTp49NmjQpp+fEdggggEAWgQMHDtjs2bNNI1vXq1cvy3oWIIBAZgF1S6CHkQ0bNmSgwcw0vEMgqMD69evt119/tRYtWliZMmWC5mEhAgj8T0AVWTZs2GCtW7e2IkXi/rPyfwdiLqTASy+9ZFOnTs20fsmSJe69WllqYGL/pHvof/3rX/6LmE+gwLx589zRmjdvnsCjcigEEPAE4v5NpX48CX56vEwRQCBeAgrmbNy40fbt20cANF6o7KdAC6hvVvXXunbtWgKgBfqTpnDxEtC1omtG1w4B0Hipsp+CLKAuzLZv3+76Aa9QoUJBLmrSlm3EiBH222+/BT0/Baj18k/qn/26666zihUr+i9mPkECf/zxhzsSAdAEgXMYBAIE4hoAVdN3PWlSuuSSS+zKK6+0+vXrW6VKlQIO+7+3aiI/cuTI/y1gDgEEEEAAAQQQQAABBBBAAAEEshVQxaO5c+eafod76dChQzZ//nw75phjrGTJkt5iNz366KMJfmYS4Q0CCKSSQFwDoApmbt682Zo2bWrjxo2ztLS0sJZqzjp8+HB77LHHwuYlAwIIIIAAAggggAACCCCAAAIImOvmRl3dBKZ+/foFLuI9AgggkPICcQ2AHn744e6JUsuWLSMKfnr6hx12mKnpPAkBBBAIJaDmiHpgohcJAQTCCxxxxBHuO7latWrhM5MDAQRM18rOnTtN1w4JAQTCC9SsWdOKFy9OlxHhqciBgBOoXbs2EgggkIcCcQ2AqhyXXnqpTZ48OeoihRolPuodsQECCBRIAXWu365duwJZNgqFQG4I6KFBx44dc2PX7BOBAinAQ7YC+bFSqFwUqFu3rulFQgCByASaNWsWWUZyIYBArggUivdehw0bZhoJftasWRHv+s8//zSNYEdCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiKRD3AGiVKlVs4sSJNnDgQFuzZk2256pan/PmzXMj0amzZhICCCCAAAIIIIAAAggggAACCCCAAAIIIBBPgbg3gdfJVaxY0b3q1KljRYsWDXm++/fvt3379rn1GgiJhAACCCCAAAIIIIAAAggggAACCCCAAAIIxFMg7gHQjz/+2Hr27GkKbip5Ac54njT7QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEIhGIewD0b3/7my/42ahRI2vYsKGVLVvWSpQokeV81Pfn2rVr7csvv8yyjgUIIIBAoMBff/1lGgypcOHCgat4jwACQQT27Nnjvn/T0tKCrGURAgj4C6Snp9vevXutZMmS/ouZRwCBEALqzuzAgQNuJPgQWViMAAJ+Al4lsexayfplZxYBBOIsENcA6LZt2+z33393P7Zef/1169WrV0Sn+95779n8+fMjyksmBBBITQEFcqZOnWrqZ7h169apiUCpEYhCYP369fbtt9/ascceaw0aNIhiS7IikJoCixcvtkWLFlnbtm3dd01qKlBqBCIXmDt3rum7pmvXrjw4iJyNnCksMH36dFf6bt26pbACRUcg7wTiGgAtV66clS9f3tq3bx9x8FNFP+OMM2zVqlV5p8CREUAg6QVU+1ODpe3evTvpz5UTRCAZBPTQQIlrJhk+Dc4hPwh414w3zQ/nzDkikJcC+n7RvZnu0ag5nZefBMfOLYEtW7aYuvhTbed4JD00UNqwYUM8dsc+Ukhg2bJlKVTa3CtqXAOghQoVcsFPNXmPJhUrVsyuvfbaaDYhLwIIIIAAAggggAACCCCAAAIIIJArAsOGDbPnnnsuV/bNThGIVqB48eLRbkL+AIG4BkC17yFDhtjAgQPd00AFRCNNn3zyiVEVPFIt8iGQegLe3xNvmnoClBiB6AS8a8WbRrc1uRFIPQHvWvGmqSdAiRGITsC7VrxpdFuTG4HkF9ixY4c7yY4dO1rt2rWT/4Q5wwIrMGPGDNu5c2eBLV+iChb3AOhZZ51lPXr0sDvvvNNGjBgRUTk2bdpkM2fOJAAakRaZEEhNAXWx0axZM9fNRmoKUGoEohOoVq2aG5RQUxICCIQXqF+/vpUuXdqqV68ePjM5EEDAmjRpYtu3bzfdo5EQKMgC11xzjfXu3bsgF5GyJblAv379bMqUKUl+lsl/enENgO7bt8/Use95551nV1xxhWk0zZo1a2aroEGTJk2aZOeff362+ViJAAII8OSV/wMIRC5QpEgRq1u3buQbkBOBFBdQH4ZcMyn+n4DiRyVQoUIF04uEAAIIIIBAfhCIawC0aNGiLvC5cuVKV/aRI0dGbEAANGIqMiKAAAIIIIAAAggggAACCCCAAAIIIIBAhAKRd9IZwQ7T0tKsf//+EeQkCwIIIIAAAggggAACCCCAAAIIIIAAAgggkPsCca0BqtNV3wT33nuvXX311da9e/ewzSI2bNhgY8aMyf2ScgQEEEAAAQQQQAABBBBAAAEEEEAAAQQQSDmBuAdAGzZsaJ06dbLRo0eb+h+LJHXo0MGeeuqpSLKSBwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiFggrk3gvaOOGjXKChcu7L0NO61cubKdfvrpYfORAQEEUltg+fLltm3bttRGoPQIRChw4MABW7p0qe3ZsyfCLciGQGoL6FrRNaNrh4QAAuEFdE+mezMSAggggAAC+UEgVwKg7du3N/UHGk0KN1p8NPsiLwIIFDyBHTt22Pz5823BggUFr3CUCIFcEFizZo0tXLjQlixZkgt7Z5cIFDwBXSu6ZnTtkBBAILyA7sl0b6Z7NBICCCCAAALJLpArAdBoC719+3Z75plnot2M/AggkEIChw4dcqX1pilUdIqKQI4EvGvFm+ZoJ2yEQAoJeNeKN02holNUBHIk4F0r3jRHO2EjBBBAAAEEEiSQ5wHQ9PR0F/zUlIQAAggggAACCCCAAAIIIIAAAggggAACCMRTILJRigKOeOedd9rnn39u//znP+3UU0/1rVU/MCeeeKIdPHjQtyy7mf3799umTZtcs4nhw4dnl5V1CCCQ4gLFixe3QoUKWalSpVJcguIjEJmAd61408i2IhcCqSvgXSveNHUlKDkCkQnoWtm5c6fpHo2EAAIIIIBAsgtEHQBVXy/33HOPK9dNN92UqT++ChUqWLVq1Wzq1KnJXm7ODwEE8plAyZIlrVu3bla0aNF8duacLgJ5I6ABBk877TR+mOYNP0fNhwL169e3WrVqWYkSJfLh2XPKCCRe4IQTTjBVaCEAmnh7jljwBFSZrFOnTlEV7LzzzrNUr0im7hQffPBBF4P6888/fX4ak6Zs2bJWpUoVq1q1qnXt2tXdF+s3pX9SH8ZDhw617777zrxWyXXr1rVevXrZpZde6p/VZs2aZU8//bTNnTvXLW/Xrp2NHj3aAveZaSPeJJVA1AHQOnXqWPny5U3/0Zo1a5alMP3793f/+fSfQMHQ7G4i9YW5ceNG27p1a5b9sAABBBAIFOAGO1CE9whkL5Ddd3D2W7IWgdQT0I8lrpnU+9wpcc4F1DKHe7Oc+7ElAv4CCt79+OOP1qdPH7vyyiutevXq7jupY8eOtnz5cnv44Yft/PPPd7GTH374wW6//Xb77bff/HeRkvOKTamCXuPGje2iiy5yBu+9954Ldq5cudIZTZkyxa0rUqSIXXvttS5o7H3flytXzp577jkXBH3kkUfc9k888UTQYHSbNm3ccWrUqGE9e/a0sWPHpqR5fi501AFQRdGXLVtmuuhOOumkLGU/++yzTbVOvv/+e3fRZskQZMH//d//mX+0PkgWFiGAAAIIIIAAAggggAACCCCAAAIFTmDPnj3WtGlTGzdunOv2yyuggnZKFStWtJo1a7pXkyZNTIG71157zctWIKe33XabjRo1KqKyHXPMMb58rVu3drUyGzRoYHqdfvrpptbLqqx333332cSJE+3LL7+0ww47zLeNund8/vnnXfeM33zzTdAAqDKrEt/evXt9raJ9O2AmXwjkaBAkXXwnn3yyFS5cOEshdSEOGDAg4uCndjBw4MAs+2EBAggggAACCCCAAAIIIIAAAgggUNAFFFRTDU/VrI4k9ejRo0C3WlAtTlWUizR5geJQ+dXFzdtvv+2aw//000924YUXZsqqYOjgwYPdsjFjxtiBAwcyrffeTJgwwTWnr127treIaT4SiOzqirJAqlYcTTriiCPcxR7NNuRFAAEEEEAAAQQQQAABBBBAAAEE8ruAmnDfcccdERejWLFi9sorr9iaNWtcE3k1k1c3hUr79u3zLdPy3bt3Z9mvlntBvoULF2bqllC1HNXq10u//PJLpvfecv+pujWcPXu26+LQf7nmVbtVx9NrxYoVvtXr16/3LV+9erVvucaU6devn+uTU10m6uWdqy9TDmYqVapkL730ktvy008/tRkzZmTay3XXXefGm1DTeQU6gyVtf8UVVwRbxbJ8IBD3AKj+U+s/VqRJ/8nU8awueBICCCCAAAIIIIAAAggggAACCCCAQHgBBRTVDaHGatEAPUp//fWXffLJJ9aqVSu3/KOPPnLL1e2gmth3797dNNDPunXr3LxiMS1atHDNwtVMXF0ajhgxwmbOnGlqWn7ccce5/EOGDHH78f9H/Za2b9/etG7y5Ml26qmn2rHHHuv25eXT+ai5vs7Rv6m6gqzqy1TLNXCn0ubNm+2pp55yQVy9V2thvfwDp1qe03TKKaf4as7Kwj+pb8/evXu7RQ899JD/Kje/ZMkS+/333+2MM87Iso4F+UMg7gFQjYQ1b968iEuvyP4555zj+w8e8YZkRACBlBLQUz/1x7J06dKUKjeFRSCnArt27XI3n3q6TkIAgfACulbUJ5iuHRICCIQX0D2Z7s3iUTMr/NHIgQACwQQUf7nkkksyrdK4LRpIKTBQp++3okWL2ueff26HDh1ygwGpNuPFF1/sAqAa5Fr9jGpEen0fqsm4BhDS+C4KgmrQn2+//dZ3LNX4VJD18ssvtzfffNPtT+t1/M6dO9v777/v8laoUMGGDRvmlvs2zpjRuY8cOdJ/kR1++OH2zjvvuD5ONcia+uvUSwHbeCR146iyKCmgGZjUV6jSnDlzMgVxtUy1P9XdY7CuILWelPwCcQ+ARlvkli1buqrH3tOKaLcnPwIIpIaAvrDV/GHVqlWpUWBKiUCMAps2bXLNmdauXRvjntgcgdQQ0LWiJny6dkgIIBBeQPdkujfjoUF4K3IgkJsCGgk9WPIf5Efrq1Sp4kZDr1evnsverVs3O++881ztTC/o2KtXL7euWrVqbuT5o446ypo3b+6rGanBsJUOHjzo+szUPv3HdNHo6ortpKen21VXXWU7d+50+RU0LF26tJv3/yfwHP3XpaWl+b+N27xqqCoFqySgsnbp0sWtf/jhh91U/yhgPH78eBs0aJBvGTP5TyBPA6C6yVTEX1Wip02blv/0OGMEEEAAAQQQQAABBBBAAAEEEEAgnwgoaKnUqVOnLGfsDSakQKZ/8oKmajavpIGEFAzVKOuBgUo1p1eQUQ8WVUs82ZL6+FTSuQdLQ4cOdYv//e9/+2qJql9S5T/66KODbcKyfCIQcwBUo2fpIvFeuiA0QrxGg9dLTyNU5VmRfY0eryrNGvSoTJky7v0///lPRxXqqUU+ccyV01QfHXqyumXLllzZPztFAAEEEEAAAQQQQAABBBBAAAEEshPwgpyq2amkAKiS+gsNlhQEVdIASzlN3jFzun2w7XT+XpeNbdq0CZbFdR2gvkpV6/ORRx5xedT8XU39SflbIOYAqPp6UAexhQoVctWgxaFRvlTVWS8NcKTRyNSPhGp8Kpinjm0V3POSqkJfdtll3lum/xVQFWv1waH+O0gIpLqAHproC1bXBAkBBMIL6GGjHjyqCRMJAQTCC+ha0TWja4eEAALhBXRPpnsz3aOREEAgtQQ0Cr1SqK6WjjzySLdefY7mNOVGAHTx4sW+ZvnqgzRY0nFvvPFGt+rll1+23377zb7++ms3dk2w/CzLPwJF4nGq119/vZ1wwgl25plnukCn+k1Q7c/skgKm+tKsX7++63fC64chu21YhwACqSugWuahvqRSV4WSIxBaQD9IO3bsGDoDaxBAIJOAAjmharJkysgbBBBwAhqUJF4Dk0CKAAL5S0Ajxyuphqf6Aw0cGGjRokVufaNGjdw0J//EOwCqAdu85u0ajDtYFwDeefbv39/uuOMO18/xWWedZeeff755QV8vD9P8JxCXAKiKfeKJJ7rRudSR7pgxY6xt27b5T4MzRgABBBBAAAEEEEAAAQQQQAABBJJAQEE7JQUZQyVvcKHAwWK9rvT27dsXatOIl3tN370N1ERcTchnzZrlRor3H3FeeWfOnGkNGza0Dh06eJu4QZDUZaJaBKtrRKVQ56g+SNWKOFhw1bdDvxk1Vw+X1IT9o48+cpX3nnnmmWyz6/gaxOmuu+6yn3/+2SZMmJBtflbmD4G4BUBVXAVBP/jgA6tatWr+KH2CzvLZZ5913QREezhd8EqffPKJ6Q9MYPKeqgQu5z0CCCCAAAIIIIAAAggggAACCORfAQ0WvWHDBleAwOCmf6maNm3q3r7xxhuu/0qNr/Laa6/5RjnX8iZNmphXa3P16tUuv/rxDIzdrFixwq3zApPecdR0XMkbOV21MxVEVBB02LBhrjZl2bJlXZ7Zs2e7rhB1XP9ak82aNXPNyW+77Ta77rrrXPBUgVK1Wlq6dKm9/fbb7vxLlSrlapfrPEePHu1Grlc+1cIMlfx91qxZ4+sCSt0yKuj55JNP2ldffWWnn366PffccxY4yFOw/SoAev/991vLli3doE7B8rAsfwnENQCqonft2jUiAV04lSpVcn2HRrRBPs6kfi9+/fXXHJdAF20s2+f4wGyIAAIIIIAAAggggAACCCCAAAIJFVDgbfLkybZ792533Icfftg2bdrkgoCBTbePP/54V+HqgQcecAG+1q1bu5qLCloqoKkBiVRDUjUZNaiPF1u46aab7OKLLzYFJJW+/PJLu/fee938999/b3fffbepu8MPP/zQXnjhBbf8nXfeMXV5OHjwYBdUnTNnjvXp08e6dOliAwcOdDU2NZbJZ599lqUrpgcffNDV6lQAUjUqe/fubY8//rgrpwKT+/fvd8fQP9dcc43rd/Pmm2+2GTNm2Kuvvupb5z+jMWdUbh3TS6qYV6NGDXcuGvFeo7erpbLKFk33UOoWp2/fvq6in7dvpvlbIC2jevJ/hvGKczlUVVlPLBT88+/4dty4ce4JgTrL1RMCjSL/2GOPWcmSJeN8Bsm1O13gGsxo165d1qNHD1N/huHSsmXL3AhltWrVCnqh+l/k4faVLOv1x1ejrvXq1cvee++9ZDktzgMBBBBAAIGECXh9WuXSLVjCysGBEEAAAQQQKMgCilW89dZbpoGfFaxL9qTm8qox6g2AqYGnvebxuX3uCrQqwKp+gTXOi3evE+y4igVpwEEvThTqPNUiVgNsB9ZSDbbP3Fq2ceNGF7eKpMZobp2D9qs+S6dMmeIC2ArKknImED4KF+V+VT34hx9+cFt17tzZFOXXEwIlRfdV1dlLqtmo6L9G1dITgoKcVF1b/aKqM131e6EnGOE6DR87dqwNGTLEWrVqlemJRkF2omwIZCeghyp6eBDYyXZ227AOgVQW0E2jbtiyuwlNZR/KjoC/gALSe/fuLfAP5f3LzDwCsQiowosCLsWLF49lN2yLAAJxEtDvJC/4qV0mKvipY9WuXdu9NB8uBQY0Q51nhQoVTK+8TGq1TCo4AoXiXRT951VHt3/88YdNmzbNF/xcsGCBqYq1l1QbUv04qE8H1XRU36EFPaka9tSpU+3cc881VUt/8cUXC3qRKR8CcRNQIEf94c6dOzdu+2RHCBRkAXU1o2tmyZIlBbmYlA2BuAmoqaCuGa9/s7jtmB0hUEAFdE+ma0b3aCQEEEAAAQSSXSCuNUA1upiqM6uJszqy9U+qyej16aC+IVS7UUm1Io888kjXMe3ZZ5/tv0mBnC9UqJDdcsstrq9U9ZUxadIk13lwxYoVC2R5KRQC8RJQ7U/1XeP1gxOv/bIfBAqqgPeDlGumoH7ClCveAt41403jvX/2h0BBE9D3i+7NdI9W0LszK2ifHeVBAAEEUlEgrjVANSq5Or8NDH6+//77rrangKtXr25PPPFEJuuePXva77//nmlZQX+jvjDVsbD6vtCIbKoZSkIAAQQQQAABBBBAAAEEEEAAAQQQQACB+ArENQCqUwusaaJ+PjVyl5c0mlmpUqW8t26qPjFTse8YOTz99NP25JNP2iWXXOK6CNATVBICCGQVUO1pJW+aNQdLEEDAX8C7Vryp/zrmEUAgq4B3rXjTrDlYggAC/gLeteJN/dcxjwACCCCAQLIJxDUA2rBhQ9eke+XKla6c6hh78ODBbpAjLVDtUAX6AtM777zjRgoLXJ4q79X0XwNH/fTTT65v0IULF6ZK0SknAhELlCtXzpo1a+ZqTEe8ERkRSGEBdYLfuHHjlP5+TeGPn6LnQECj1uqaUWslEgIIhBdQKzbdm+kejYQAAggggECyC8S1D1DV4rzmmmvs6KOPtlNPPdV+/PFHW7VqlTNQU+9nn302i8f48ePt1ltvtVdeeSXLulRaoJHQpkyZYo8++qgbROquu+6yYsWKpRIBZUUgrIBGFyQhgEBkAhoJtG7dupFlJhcCCLg+DLlm+I+AQOQCyTBCc+RnS04EEEAAgVQXiGsAVJh33HGHbdq0yTXt9nDr1atnr7/+uu+HmJq8q9bnm2++aV999ZXLVr58eS97yk7T0tLshhtusJNPPtk0QNKGDRtS1oKCI4AAAggggAACCCCAAAIIIIAAAgggEA+BuAdAVQv0qaeesmHDhrlm3RrdvE2bNpn6+FS/oKoRevXVV7uXClK5cuV4lKdA7EPNSebMmWP33nuvGxyqdevWBaJcFAIBBBBAAAEEEEAAAQQQQAABBBBAAIFEC8Q9AOoV4KijjjK9giX1saQXKbRAiRIl7J577gmdgTUIIIAAAggggAACCCCAAAIIIIAAAgggEFYgroMghT3afzPs378/0qzkCxB45JFHXGB5yJAhAWt4iwACCCCAAAIIIIAAAggggAACCCCAAAKBAnkSAG3evLkbJOmBBx6wefPmWXp6euB58T6IwLZt29yAUStWrLCxY8fa119/HSQXixAouALLly83XQckBBAIL3DgwAFbunSp7dmzJ3xmciCAgLtWdM3o2iEhgEB4Ad2T6d6MhAACCCCAQH4QyFEA9IsvvrDnn3/e9fWpUcuffPLJqMo6ffp0V4vxtttusxYtWrj+PzUaPCl7gXLlyln16tVdpqJFi/oGlcp+K9YiUDAEduzYYfPnz7cFCxYUjAJRCgRyWWDNmjW2cOFCW7JkSS4fid0jUDAEdK3omtG1Q0IAgfACuifTvZnu0UgIIIAAAggku0CO+gB94oknbMKECa5sHTp0sPPOOy+qclaqVMmeffZZ32jnGjVeT9xJ2QsUKlTIFHx+8803rWvXrla1atXsN2AtAgVI4NChQ6403rQAFY2iIJArAt614k1z5SDsFIECJOBdK960ABWNoiCQKwLeteJNc+Ug7BQBBBBAAIE4CeQoAOode9y4cda3b1/vrWvO/thjj/neB86otqhqMXrp4osvtlmzZpmWp6WleYuZZiNQs2ZNu/nmm7PJwSoEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8ARy1AReGx977LGZgp9aVq9ePevRo4drBvHSSy+ZXq+++qppRPMLL7zQSpcurWyZ0iWXXJLpPW8QQACBYALFixc31YIuVapUsNUsQwCBAAHvWvGmAat5iwACAQLeteJNA1bzFgEEAgR0rejeTPdoJAQQQAABBJJdIMc1QI8//vgsZStbtqxdcMEF7jVo0CB74YUXbPTo0XbVVVdlyestaNmypZUpU8Z7yzQJBTRI1bp16+IyWNX+/ftdCTXAgAbmKFasmBUuXDhoqdWc5q+//nLrVENYN1ehagprf96+tT/tN1TSPr2mOhw///jrQUq3bt1M/d8GJj5//v9z/f/nb6v/37/KlSvbaaedlumHKX//kufvf+DfMf/3fP/lzfd//fr1rVatWu4ewhs8jPsP7r+4/wx9/92sWTPfQHv79u3j/pvfH/5fZZnm8+v9B4M1Z/oYeYNAvhfIcQC0YsWK2RZeP7oUAO3evXu2+fTUUD/SSMkrMHjwYNdnazzPcPHixfbJJ5+4GyUFtYIFQWfOnGnqH9ZL+lHSvHlz761vqh8p06ZNs4MHD/qWtW3b1qpUqeJ7781opEp11u4lBUA5Pv78/8v6EILrr2D8/dGDAy/x9y+5/v7rc9H3Fn9/kufvj4KdumZmzJjB/Qf3X96fThcU5/6T+2/ff4j/zvD7IzV+f23bti3wo88X78NVNPIvhB66KiYTTVJgOFTFpFD72blzp/3000/Wrl27UFmSarnK+Msvv9hxxx2XVOfFycQmkOMAaLDm7P6n4gU1van/usD5ChUqBC5Kife7d++2jz76yA0AtWrVKlu9erXrPuDwww+3I444wo466ijr3LmzqZZssB9IiUJq2LCh1ahRw1drMpbjbtmyxfbu3etGs9ePDNX+DfXHU/3F7tq1y3e4UDWFZaN1Xm1RvQ/VFKdkyZKml/c0j+Pjz/+/4P0vc/3x94e/v7n7/aMvN/7+8PfHd5PjN8PfX/7+8vc3d//+cv/P/X+k379FiuQ4XOL3Vz1xs+PHj7eRI0faokWL3O/hNm3a2OOPP27BWu++99579vrrr7sHfxs2bHCBvltvvTXbymlbt261Bx980BS7ePnll8MWTL/977zzTlu2bJm1b9/eTj/99Gy30Tk98sgj5gWe9TmdddZZNmDAAKtbt26mbVXOiRMnunjBYYcdZrfccoudccYZmfLE8ubuu++2f/3rX/bQQw/Z0KFDY9kV2yaTQEYgKOp0/vnnp990003Zbvf111+nZ5QzPePpQ7b5tLJTp07pw4cPD5uvoGTIqK2Y3q9fv/SML19nJKfsXhk3wumXXXZZ+q+//prvCW644QZX1ow/bPm+LBQAAQQQQACBnAh43/k52ZZtEEAAAQQQQCAxAr1793a/Xd98883EHDCGo9x///3pGZXP0jMGmk4fOHBgerVq1dy5Z1Q6Ss+oyZhpzxmBxvSM4G76d99955ZndCfm4g0ZrSiDxm8yApLpGYHA9KpVq7p9nnPOOZn2F/gmo1Zp+jPPPOPOZ9iwYekZFaACs4R8/8cff6Qr/qF7pS5duoTMpxUjRoxIz2jRmaV82W4U4cqM4LA7/w8//DDCLXI3W8bg4+kZleTSMwYiz90DFfC9R1fXOeN/YW4k9d+XCklPVs477zw75ZRTLOM/bqbajdmVf8eOHa47AQ08pf5UU8UrOxPWIYAAAggggAACCCCAAAIIIJDqAqrx+cEHH7gm5q+99pqLHaj5tmpdqvWlanZ6SXkzKmO52pInnHCCW6wWlLfffrvNnj3brr76ai+rb6p4xHXXXWdPP/20b1l2M6qNeeWVV9q1115r9913X8jWmcH2UbNmTbv++uvdqh9++MHUajZUUk1RleWYY44JlSXs8hUrVgQt10UXXWTr16+3nj17ht0HGfKPQFLU6fYGr8g/bNGfqfqyzKjpahm1ON3G6s9SVdHV4b4uco2iqJeahavPDvWRoZf+2Ggb/TFSE/mnnnrKfvvtN/v3v/+dbUfj0Z8hWyCAAAIIIIAAAggggAACCCCAQDwFVIHp0UcftVdeecUyaji6voUvvfRSF1SMRzP7jBqqNmbMGKtUqZLvtNWdipqTqxn8rFmzfMsVT1A3G0OGDPEt04yamHft2tVV1MqoTeq65PMyKF6hpK76wiXFKdRMXvtSUDUnSQFQ7UNN7tWsX8HUwKS+S1VuNZvPadLgbRm1fO3MM8/M6S7YLp8J5DgA6vW3GI/ybty4MR67Sep9DBo0yPX1ec0119gVV1xhTZo0ifp816xZ456gPPnkk/bEE0/QF0XUgmyQnwV046AHAepXuF69evm5KJw7AgkR0M3t999/b+rHOdigcAk5CQ6CQD4SUE0PPXRu0aKF61s8H506p4pAnggsXbrU1MKtdevWFo8gTp4UgoMikMsCGvCwV69eNmnSJN+RVHMxo0tB+/zzz10AL9bxPtTCtGnTpr79ezNapn40NfCvUkbrZpswYYKbDza4m2IUGqhYgcVgNUHdhtn8o9qUCuwqKYAZ7eBK3q4VaNV+VON09OjRLn4S2G/rZ5995oK0Gi8lVFIAdcmSJVanTp1MwWHlVzzrwgsvdL8vM5ram2JScipfvrxvd/rsFIPxAsDeCg0smtEdgKvZ+ueff9qCBQvcvUOocUgUrNV5qO9UGfsHqr19Mk2MQI4DoKpindGvhKu1GOxU1dGtkgJ1RYsWDZbFXYAaBEj/gQL/QwfdIJ8uVPXzjz/+2KZPn24dO3bMcSnk/dhjj7knFKqSndGfZo7/qOT4JNgQgTwSUDBHX0x6UkcANI8+BA6brwTU8kA3fmvXriUAmq8+OU42rwR0reia0bWjQVJICCCQvYB+zG/fvt3VJkvVQW2zF2ItAmbPP/98puCnv4lqS7744ot2+eWX+y+Oer5Dhw5Bt9H1qaCnHuwpKZinl1qeBns4rkGYlTL6BnXTaP9RjVN9j6rWqeI7Cl4q1qOu/BRsjObvxI033mhjx451I7ErZhQ4wNFLL73kAqPBzvHHH3+0wYMHu8ClRnFXLVEFPLU/tcpV0iBOeoij5HUfoPP+5z//6WrMvvXWWy4QrCbwqjWrpviqxfvuu+/anDlzXEBT5dXnpxa8Kts333zjyup2+t9/FAvq37+/C4LLPaMvTzcos7ofOPLII11NWQ30REqMQI4DoCtXroyoSvPf//73xJQkiY+iKue6AGMJfvoXr1u3bu5px++//55lNDT/fMwjgAACCCCAAAIIIIAAAggggEDeCLzxxhvZHljrYw2AhjqAF8j0YjLr1q1zWUM95CtdurRbr673cpK8pvaq4frCCy+4AN/PP//sAof33HOPvf/+++b1Oxpu/w0aNHD9b2YMQuRGYvcPgKqbwClTpphaxgYmtRhUkFNdAmQMJO1WZwzE5JZ17tzZBTBVI1fN6lWxRv2UZgwa5UaR9/ZVsWJFU+1PfwfV7lRTeQVDldSyVy+NRq9KahrtXkFbnZeXFBhVcFPdAfzjH/9wi9VfaY8ePVwQVLVcqdjjaSVmmhSDICWmqHl3FDUNadSoUVxPQE8n9NSVhAACCCCAAAIIIIAAAggggAACySfgBR1DnVm49aG2i2T5Aw884IJ7GgxJafPmzW5asmRJNw38x1u+ZcuWwFVh32tcl3nz5rl8qhWp2p8afElN6hUYVDBxwIABUQ3orG4ClNSS1tu33quJvoKR/s3VtVxBS1U8U+1WBTW9pHFW1JxetWE1qLTGWvFPga2RNU6LKp35J3VToDFc9FLSQE/eOSjAqgCpBm3yT5MnT3a1TFUL1UvarwLNahJ/2mmnua6qvHVMc18gxzVAdWr64NT3gf5D5SSpvwQ1N9LIZAU51ahRw7744ou4FVEXri6uYP18xO0g7AiBJBPQk0r1/6kXCQEEwgscccQRpifY6j6FhAAC4QV0rehHka4dEgIIhBdQv3j60R+qNln4PZADgYIvoBp+agYdKuVWDcC3337b1PekV2NRx/e+3xQoDJa8cV6OPvroYKuzXaYyanvFhtQvsH/SSPDqCmDhwoU2Y8YMO+mkk/xXh5xXTU7VGFWT84cfftgN0KTMCrD+3//9X5btfvrpJxcnUY3LwKCmugFQU3ydp5qqK/iYXQrVL6vX37F/DEx9h+rvoZrUK1bjHdsLJKuPUC9pv/JRUFddvEXTLYC3D6Y5F8hxAFQjc+k/Xaj+PSM9JfWloP4sC3JSbU097Tj99NNd3w+xlvWuu+5y7lwssUqyfX4S0JdNu3bt8tMpc64I5KmAfpDGq+uVPC0IB0cgQQI8ZEsQNIcpMAIaNVovEgIIhBZQU2v19RkqBRvhPFTeSJer2bmCjhqHxH9gnlq1arldqCJasOTVjMxJ61VvhHgN8BMYPFSAUDUnFfzUYIORBkB1jkOHDrU+ffq4Wp8qk4KGamJ/4oknZimCAqBKoSrMKAiqAKgCsf4BUC9gmWWHUSwItg+vFumnn36aaU+q9avBm4jnZGJJyJscN4FXld9Yg58qoTqCzckIYwnRidNBdGOgvh/OOecc6927t02bNi2qqt86DT2l0YWjC1UB0Nz4Qxmn4rIbBBBAAAEEEEAAAQQQQAABBFJeQM2kb7/99qAOd9xxh+sPMujKHC5UM3c1837nnXd8NT69XakGqFrxanCkYK1wtVzJv8m2t224qTdAtsaK0WCCgclrOh4sUBiY1//9BRdc4GpXqom9+trU4Eeh+kz1RrtXK+NgSYMOKQXGsaI9p2D7DrZMAedRo0a5rgvvv/9+F9NRLEgBXNViJSVeIEc1QE855ZS4NqlTJF41QQtyUp8TeiKhquh6qb8KPVlRlXeNtqY/RAoGq9+NAwcOmJ7KyER9ZegphUYy86pQqwPdIUOGFGQuyoYAAggggAACCCCAAAIIIIBAvhe499577dRTT7VXXnnF/vjjD1NgTAPgRFMTMhIExQ+uuOIK19+lN6K7t50G5FFt0Isvvtiee+45W7RokTVr1sxb7aZeDcrmzZtnWh7JGwUR1fJItU41GJL/oEXa3ut/NNrapWoFqNbHN998sz3zzDOu1qQ3uFPgeTVu3NgtUg1PVSALrImqMisFnkNuBUB1LPUPqvNZsWKFm69du7aphi61P6WT+JSjAGi8g2+qJq3akQU56WmL+nlQp7yqAq+nK+p7Qq9Ik4KjqgI+YsSISDchHwIIIIAAAggggAACCCCAAAII5KGARiDXK7eSakiqZqRGI9dI4/5J/VIqgKgalIonqD9O1aTUQEX+SYFLtTjVIEA5SWqirlarM2fOzBIAVfN31QJV94DRJgV11QpWNSfVrNzryzRwPyq39q9yaDR2/yCsDHReDRs2tA4dOrhNvX48Nap8YFL+aFKo/Gq5qxiQd8xo9kne+AvkKAAa/9NIjT1qwKgPP/zQXYzjxo1zgVCvn43sBBQgVjcBqsqueRICCCCAAAIIIIAAAggggAACCCCgwY769etnc+fONTWr909q6q4WpQoiqkakBgLS6OyPPvqom3rNwsePH+9qaWbXX+nixYvdrtevX+9/CN+8apSquf/jjz/uRmH3+gj+9ttvXbP4V199NUutTN/G2cyUK1fOBXcfeeQRV45QWVWTU7VEFQRVzUsNolS2bFmXffbs2aZA5xtvvGFeU3nv/NRdQN++fV3gtEePHnbYYYe5YKs23Lp1a6bDqSanktc6V/My9pbLxjNVwFW1bVetWuWC3yqHKrWpv9QmTZqYaoOSEitAADSx3u5o3bt3N710oUydOtWWLFli6qdCneHqolSgVNXi9dJoYq1atXIXSh6cKodEIKkENLKgmkEENmdIqpPkZBBIIgE1d9LT7dxs2pNExeVUEIhJQLU3dG+mHyckBBAIL6Ampuq6y3+QlfBbkQMBBOItcMMNN7hBgrRfjUQemAoVKmSDBg3yLR45cqRVrFjRunTp4mpUqvs9bffJJ58E7f9z+fLl9sEHH/hGlFcrVgUY1eRdfZz6p3vuucd186e+OzVQkYKNqv35+eefxzSgrZrBT5w40dQdY3ZJgUWNGq+Bk1S+gQMHuubwCvB+9tlnmQYIbd++vYu1qEm9Yi6vv/66C5gqiPrkk0+6w0yaNMluu+02Vxlt7NixrmtCrVC3Bvrbp+b0ql2rGrhKqrT2wAMPOAOt035VG1Uv/6TPRGXS6PakxAmkZdzsRVe3N3HnxpEKoMCNN97oqtrr6Y3+UJMQiFRAgRw9MKhSpYq1bt060s3Ih0DKCugJtJ6460l/gwYNUtYhGQvuBaS5BUuuT0cj06p/sLZt27rvmuQ6O84GgeQTUI0qfdd07dqVBwfJ9/FwRnEQuPDCC+2tt95ywUUNZlwQ07Jly6xMmTIhR06PpcyqMaoamKrgFY/k9Z8a6b5UK1P9baqmp5r1e/df/tvrXkwGqo2pijbxTAosKziqGIia76sfVPXTqt+1GutF48To961XEzW7Y6uGr4KoiqOotiopZwLx/YRzdg5sFURg3759tmHDBvdUgWbvQYBYlHICqv2p5h0FfcC0lPtgKXCuCejmSolrJteI2XEBE/CuGW9awIpHcRCIu4C+X3Rvpns0ak7HnZcdIpAQgaOPPjrXjhPvB/BqIRtNUlAzXDNzBUUjCUBGc1zlVYuSnj17mmrChnJQLdxdu3ZFu2vyxyBQKIZt2TQXBebNm+eav+uiISGAAAIIIIAAAggggAACCCCAAAIIJL+AamtqEGxvoKXAM1ZtUNWib9q0aeAq3ueiADVAcxGXXSOAQPwE1E+KkjeN357ZEwIFU8C7VrxpwSwlpUIgfgLeteJN47dn9oRAwRTwrhVvWjBLSakQQACB6AXUnU716tVNo8BrYCl1SaXaqOoWQKPUq5Xv888/H/2O2SImAQKgMfGxMQIIJEpAo+ZpZMHy5csn6pAcB4F8LVCtWjXXIbumJAQQCC+g/sFKly7tfrCEz00OBBDQYCPbt2833aOREEAAAQT+J6CR4NWqd/LkyW7wJQ0iVblyZWvcuLEbE6Vly5b/y8xcwgQIgCaMmgMhgECsAuH6cIl1/2yPQEESUEfuudGnUUEyoiwI+AuoD0OuGX8R5hHIXqBChQqmFwkBBBBAIKuAann279/fvbKuZUleCNAHaF6oc0wEEEAAAQQQQAABBBBAAAEEEEAAAQQQSIgAAdCEMHMQBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgLAQKgeaHOMRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgIQL0AZoQ5ugPos7EO3fubMcdd1z0G7MFAggggAACCCCAAAIIIIAAAggggAACCDgBAqBJ+h/h2GOPtenTpyfp2XFaCOSNwPLly11n+3S4nzf+HDV/CRw4cMB0zVSvXt00uAsJAQSyF9izZ4+tXr3ajjrqKNMgYiQEEMheYNu2baaXrhkSAggggAACyS5AE/hk/4Q4PwQQcAI7duyw+fPn24IFCxBBAIEIBNasWWMLFy60JUuWRJCbLAggoGtF14yuHRICCIQX0D2Z7s10j0ZCAIHIBBLxW0YP9A4ePBjRCR06dCiifF6mXbt2ebNhp9HuO+wOE5ghPT3dfv755wQekUMlQoAAaCKUOQYCCMQs4H2BetOYd8gOECjgAt614k0LeHEpHgIxC3jXijeNeYfsAIECLuBdK960gBeX4iEQs8DSpUutQ4cOtmzZspj3FWwH48ePd13olS5d2sqUKeO61Pvxxx+DZbX33nvPevfubQMGDLDu3bvb0KFDbcOGDUHzrly50i699FJTN31ly5a1GjVq2IgRI0ytjQLT+vXr7bbbbrMzzzzT+vfvb7169bIJEyYEZgv6XufUqVMna9KkiXs1bdrU/vnPf9pvv/2WJf/IkSOtffv2Lt9JJ51kkyZNypInlgV33323NWrUyB5++OFYdsO2SSZA+54k+0A4HQQQQAABBBBAAAEEEEAAAQQQKFgCN910k+3cudM0VbAvnumBBx6whx56yE455RRr27atffzxx/bFF19Y69atbd68eXbMMcf4Dvf++++74OfMmTPthBNOcLVFr7zySjv77LNdN3wlSpTw5d24caN17NjRatasaX379nUti6ZOneoLTL744ou+vH/99ZcLeCrvu+++a8WKFbNFixZZ8+bNrVChQnbuuef68gabOeecc9z5NG7c2NUs79Kli91zzz3Bstrtt99uqqWpQKVqovuXL+gGUS5s2LChVa5c2erXrx/llmRPZgFqgCbzp8O5IYCAT6B48eLui7NUqVK+ZcwggEBoAe9a8aahc7IGAQQk4F0r3hQVBBDIXkDXioIaukcjIYBA9gKffvqpffjhhy6TApDTpk3LfoMo1irI+MEHH9hPP/1kr732mr3wwgv2yy+/uBqSe/futVtvvdW3N+Xt16+fnXHGGS7YqBWFCxd2AcXZs2fb1Vdf7curmRtvvNEFGb/66isbM2aMqRxvvfWWy/PSSy/ZDz/84Muvbb/99lu3LwU/lRSYvPDCC90xdexwScHT66+/3mXTvnfv3h1yE/VBrLLEEvxcsWKFPf3001mOcdFFF5lqs/bs2TPLOhbkXwECoPn3s+PMEUgpAQ3i0q1bN98XdUoVnsIikAMBPbU+7bTTeHKdAzs2SU0B1fLQNaNrh4QAAuEFVHNM92YMtBfeihypLaCm4gok+qcbbrgh4n46/bcLNv/mm2+64GSlSpV8q9Vc/ZFHHnHvZ82a5Vv7izlDAAAwZklEQVT+1FNPmfrxHDJkiG+ZZurWrWtdu3a1cePG2aZNm9y6P//809LS0lxTdv/MF1xwgZ1++ulukbdv1RRVQLRNmzbWrFkz/+w2ePBgF8gcO3ZspuWh3igAqr8rW7duNTXrD5bU9YbK/be//S3Y6oiW7du3z9WE1bmTUkOAAGhqfM6UEoECIeDVAi0QhaEQCCRAQE2YdONKQgCB8AK6Vvyb/YXfghwIpLYAtT9T+/On9JELqIahBtnzT6qtGazmoX+eSOfV7F39ZQYmLdN3m1cbU03Gvf441Sw9MKnvzf3797vAotZt377d7rrrrsBs7r23vbdvNXnXwEvecv+NtF+l119/PaKg7+GHH+76HNU2o0ePdk3dNe+fPvvsMzviiCOsZcuW/oszzSuAqlqtwQKcaq6vPlC1XoFe5VF5/ZPKo/5PA9Py5ctN2ytpW3Ul4L0PzKv3Ctb++uuvrtZvsHMJtg3LckeAAGjuuLJXBBBAAAEEEEAAAQQQQAABBBBIYYEtW7bY8OHDgwpouYJ0sSYNrBQsKaCnoGeLFi3c6jVr1phe6r6iSpUqWTY56qij3LLvvvvOTatVq2ZHH310lnxa4J23t+85c+a4fHXq1MmSX4MyKVipJuXBAopZNshYoBqzCt6qKf9HH32UJYtqm15xxRVZlmuBBn7SAEmq5Tp58mQ79dRT7dhjj7Uvv/zSl//ll182DUqlpO4DBg4caI899ph7r1qt6qe1du3apsGWlNQUf9SoUdaqVStTGVUONflXrVsd68gjj3Tn6jL7/aPzV61YBZ4XLFjgBm2SqYKv1113na9bBL9NmM1FAQKguYjLrhFAAAEEEEAAAQQQQAABBBBAIDUF7rzzTlMQNFjavHlzyOBosPzRLvMCmX//+9/dpuvWrXNTjRAfLClQqbR69epgqzMt077VBYZX89Tbt0aJD5ai2be2b9Cgga//TQ3u5J927NhhU6ZMsT59+vgvdvOq0akg5eWXX+5qsirIrH5JdV6dO3c29b+qpEGfvG4AFPycOHGiG9hJ6ypWrOhqqvo7qCWiRrZXEFfpmmuucd0ArF271vWRqv5IA7s52LNnj5111lmuC7d//OMfbr36Z/39999djWB1u6PBlkiJEyAAmjhrjoQAAggggAACCCCAAAIIIIAAAikgoGbv4Zq5q0/On3/+OVc0NDK8gnuqoaikgKtSqH57veWhArZu44x/pk+f7kZ39/oY1fJ47ds7hqaqhamk42kkey+p708FI8uXL+8tclM1WVd/o6rdqnJ7Sd3b6HNQbdirrrrKdu7c6a1y08DuotQnuIK7/kkDRR1//PHupeW33HKL7xyGDRvmBoPzHxBKeVT7VLVMjzvuOL11SftVMHjJkiWu33ECoJ5MYqYEQBPjzFEQQAABBBBAAAEEEEAAAQQQQCBFBCIZ6CjYAEnx4Hn77bdd35MKsHpJzdCVFCgMlrx+LEM1e9c26iNUNUo12rx/YC8e+w48p06dOvkGwH344Yd9q1988cWgzd/Vr6qCkKo9GhjUVFN9NYNXjc1vvvnGt69QMwp4BktFihRxi/37DFc/qBq9XrVDFWT1khdIVtN3L2m/rVu3do4ajIqUWAECoIn15mgIIJBDAd0c6MvK66slh7thMwRSRkA3VerryGuqkzIFp6AI5FBA14quGX6Q5BCQzVJOQPdkujfTPRoJAQQyC6hfyalTp2ZeGOLdJ598Yv/+979DrI1+sWqU3nffffbee++5moneHmrVquVmNXBPsOTVjGzUqFGw1W6ZalD27dvX1zzdyxjJvhU8VHAymjR06FCXXbU+1SRdgwmpufmJJ56YZTcKgCpVrlw5yzot8PorDRyQKjBYGnTjMAuD7cOrRfrpp59m2lrdBWjwpgoVKmRazpvcF/hP+Dr3j8MREEAAgZgE9INUo+bt27fP6tWrF9O+2BiBVBDYtGmT66BeT7qDdXSfCgZ5XUbVktCPH/3dCpb048g/qQZB165drWjRov6LmU+QgK4VDeqgaydU/2gJOhUOg0C+EFi1apUbNVn3aPyQzxcfGSeZIAF973vNtyM9pAJ96hPSG1U90u0C86kpuoKU77zzjht4yH+9ammq+bUGR9q7d6/512JUPm8UdP+anf7bP/roo+6eUjVbA5M3gFKwB++qFal+O9W0PNp7nAsuuMDUxFyDDmmQokKFCrn+PQOPr/eenb7PgyUNVKQUeA7BgpfBto92mYLCGjjpjjvusPvvv99uvvlm+/zzz10AVwFdUuIFCIAm3pwjIoAAAggggEAKCOhGXTe7oVKvXr2yrHrwwQej/tGUZScsQAABBBBAAIE8E9AD0Lfeeivq42s7L4gX9cYZG2ikco2Mrv4uvYCktx8NyKOBfC6++GJ77rnnXB+ezZo181a7qVeDsnnz5pmW641qk65YscL8m6J7mVSjVIHKW2+91Q3u4y33purvUkHhYPv18oSaqtbo9ddf7+6nnnnmGfewxRvcKXCbxo0bu0Wq4alm/oHN2BctWuTWB9Zwza0AqA6m4K3OR3aa18jyqqHLQyP3UST8HwKgCSfngAgggAACCCCQCgKnn366zZo1K0sNUP0QUFJNCP+kHz09evTwX8Q8AggggAACCOQzAdWy9JpbJ+rUFTzVyOcadf6YY47JdFjVwNQDWT2YVU3T559/3l566SUbPXp0pny6Z1Et1MD7k2nTppma6Y8ZMyZTfr2ZOXOma5Y+YMAAFwTVKOtq4u3VtlQe7Vfp2muvddNo/1FQ96677nI1J9Ws3OtvNHA/KnebNm3c8TRK/BlnnOHLIgOdqwYd6tChg1vu1YBV7dTA5N+XZ+C6YO9D5ddo8xqYyTtmsG1ZljgBAqCJs+ZICCAQg4CaI6o/l1B9usSwazZFoEAK6OawYsWKVq1atQJZvvxQKDUhy0kNkPxQtoJ4jrpW1P9ZqB9WBbHMlAmBWAQ06IdqlNFlRCyKbItA7AKHDh2yfv362dy5c11za/89qqn7L7/84mqGqkakBgJSTU01Z9fUC1SOHz/ejeQe2BepgpfnnnuutWvXLlO/nwr4rVmzxjVN//33390hR4wYYV9//bVp9Hmvpqhqfqo/UgVn27Zt639qEc+XK1fOba9R5xUMDZVUk1O1RBUEVW1LDaJUtmxZl3327NmuGf4bb7zhq2Vbt25dt07dBahfU5VVD6IPO+wwF2zVSnWN459Uk1PJG+BI8zL2lqsLAM9UAVfVtlV3IZ07dzaVo2TJknb44YdbkyZNXG1QbU9KnAAB0MRZcyQEEIhBQM0f9MVLQgCByAT0g7Rjx46RZSYXAgjwkI3/AwhEKaDggRdAiHJTsiOAQBwF1Cen16dksAFj1W/moEGDfEccOXKke0jepUsXU41KNWHXdqrl6d//p5YpIKgakh9//LFve/8ZBSS9IKP+HsyYMcP69+/vAn7av/pCP+uss+yee+7x3yzqeTWDnzhxop1yyinZbqvA4pw5c6xPnz6m4w8cONA1h1eA97PPPst0b9y+fXtr1aqVqUm9pq+//rori4KoTz75pDvOpEmT7LbbbnP9qo4dO9Z+/PFHt/zee+91D4DUnF61a1UDV0n9ryoArDErtE77VW1UvfyTPhOVyQsU+69jPvcE0jIi9+m5t3v2jEBmgRtvvNFVtdfTm2CdJ2fOzTsEEEAAAQQQQAABBBBAAAEEEi9w4YUXupYcCi727t078SeQgCMuW7bM1eKOdys7BVVV87FOnTq+GpexFuePP/4wb7T5SPalWpnqb1OBWTXrD9bXp8JhMlDfnKpwE88kAwVHFQPRyPUaoEr9tKo/Vo1or75aFSCO5EGSavgqiKo4imqrknImEN9POGfnwFYIIIAAAggggAACCCCAAAIIIIAAAgkUOProo3PlaOoHVf1txjNFE/zUcRXU1Cu7pKBoJAHI7PYRbJ2axffs2dP1i9qgQYNgWVwt3F27dgVdx8LcESiUO7tlrwgggAACCCCAAAIIIIAAAggggAACCKSWgGprTp8+3byBlgJLr9qg6pe0adOmgat4n4sC1ADNRVx2jQACCCCAAAIIIIAAAggggAACCCCQOgIa8Kl69eqmUeA1sJQGn1Jt1MWLF7vBlipVqmTPP/986oAkSUkJgCbJB8FpIIBAeIG//vrL9c2iEQxJCCAQXkB9DOnJc7A+j8JvTQ4EUktA/YCpyZpGaCUhgEB4gYMHD9qBAwfcQCDhc5MDAQQQSB0BjQQ/b948mzx5sht86YMPPnCDLTZu3NiNidKyZcvUwUiikhIATaIPg1NBAIHQAgrkqJPoKlWqWOvWrUNnZA0CCDiB9evX27fffuueOIfqewgqBBD4n4BqZSxatMhUa0PfNSQEEMheYO7cuabvmq5du/LgIHsq1iKAQAoKqJZn//793SsFi5+URaYP0KT8WDgpBBAIFFDtz0OHDrmR8wLX8R4BBLIK6KGBkkabJCGAQHgB75rxpuG3IAcCqS2g7xfdm+kejYQAAggggECyCxAATfZPiPNDAAEEEEAAAQQQQAABBBBAAAEEEEAAgRwLEADNMR0bIoBAIgUKFfrPnytvmshjcywE8qOAd6140/xYBs4ZgUQKeNeKN03ksTkWAvlRwLtWvGl+LAPnjAACCCCQOgL0AZo6nzUlRSBfC5QrV86aNWtm5cuXz9fl4OQRSJRAtWrVbP/+/aYpCQEEwgvUr1/fSpcu7UZtDZ+bHAgg0KRJE9u+fbvpHo2EAAIIIIBAsgsQAE32T4jzQwABn0Dt2rV988wggED2AkWKFLG6detmn4m1CCDgE9Do71wzPg5mEAgrUKFCBdOLhAACCCCAQH4QoAl8fviUOEcEEEAAAQQQQAABBBBAAAEEEEAAAQQQyJEAAdAcsbERAggggAACCCCAAAIIIIAAAggggAACCOQHAQKg+eFT4hwRQAABBBBAAAEEEEAAAQQQQAABBBBAIEcCBEBzxMZGCCCAAAIIIIAAAggggAACCCCAAAIIIJAfBAiA5odPiXNEAAEnsHz5ctu2bRsaCCAQgcCBAwds6dKltmfPnghykwUBBHSt6JrRtUNCAIHwAron070ZCQEEEEAAgfwgQAA0P3xKnCMCCNiOHTts/vz5tmDBAjQQQCACgTVr1tjChQttyZIlEeQmCwII6FrRNaNrh4QAAuEFdE+mezPdo5EQQAABBBBIdgECoMn+CXF+CCDgBA4dOpRpCgsCCGQvwDWTvQ9rEQgU4JoJFOE9AtkLcM1k78NaBBBAAIHkEiiSXKfD2aSKwKhRo2zs2LGpUlzKGQeBgwcP2u7du61QoUJWunTpOOyRXSBQsAW2bt1qGzZscNdM4cKFC3ZhKR0CcRBIT083vdLS0twrDrtkFwgUaAF1F6Fr5qijjrLixYsX6LJSuNQUWL16tSv40KFDbfjw4amJQKmTQkCtU4oVK5YU55KfT4IAaH7+9PLhuesGSUk/yvUiIYAAAgjkroAeHuhFQgABBBBAIDcEfv/999zYLftEICkEVPmC365J8VGk/Emou5HatWunvEMsAGkZT+3SY9kB2yIQrcBvv/1m+/fvj3Yz8iOAAAIIRCHw1ltvudoK3bt3t2uvvTaKLcmKAAIIIIBAeIHrr7/eDRw2YcIEa9SoUfgNyIEAAgggkGOBMmXKWI0aNXK8PRuaUQOU/wUJF6hbt27Cj8kBEUAAgVQTOPLII12Ra9WqZT169Ei14lNeBBBAAIFcFrjzzjvdEdTC65hjjsnlo7F7BBBAAAEEYhNgEKTY/NgaAQQQQAABBBBAAAEEEEAAAQQQQAABBJJYgABoEn84nBoCCCCAAAIIIIAAAggggAACCCCAAAIIxCZAADQ2P7ZGAAEEEEAAAQQQQAABBBBAAAEEEEAAgSQWIACaxB8Op4YAAggggAACCCCAAAIIIIAAAggggAACsQkQAI3Nj60RQAABBBBAAAEEEEAAAQQQQAABBBBAIIkFCIAm8YfDqSGAAAIIIIAAAggggAACCCCAAAIIIIBAbAIEQGPzY2sEEEAAAQQQQAABBBBAAAEEEEAAAQQQSGIBAqBJ/OFwaggggAACCCCAAAIIIIAAAggggAACCCAQmwAB0Nj82BoBBBBAAAEEEEAAAQQQQAABBBBAAAEEkliAAGgSfzicGgIIIIAAAggggAACCCCAAAIIIIAAAgjEJkAANDY/tkYAAQQQQAABBBBAAAEEEEAAAQQQQACBJBYgAJrEHw6nhgACCCCAAAIIIIAAAggggAACCCCAAAKxCRAAjc2PrRFAAAEEEEAAAQQQQAABBBBAAAEEEEAgiQUIgCbxh8OpIYAAAggggAACCCCAAAIIIIAAAggggEBsAgRAY/NjawQQQAABBBBAAAEEEEAAAQQQQAABBBBIYgECoEn84XBqCCCAAAIIIIAAAggggAACCCCAAAIIIBCbAAHQ2PzYGgEEEEAAgaQUKFeunDsvb5qUJ8lJIYAAAgjkWwHv+8Wb5tuCcOIIIIAAAikhkJaekVKipBQSAQQQQACBFBI4ePCgTZ482U466SQrW7ZsCpWcoiKAAAIIJEJg5cqVtmzZMvc9k4jjcQwEEEAAAQRiESAAGose2yKAAAIIIIAAAggggAACCCCAAAIIIIBAUgvQBD6pPx5ODgEEEEAAAQQQQAABBBBAAAEEEEAAAQRiESAAGose2yKAAAIIIIAAAggggAACCCCAAAIIIIBAUgsQAE3qj4eTQwABBBBAAAEEEEAAAQQQQAABBBBAAIFYBAiAxqLHtggggAACCCCAAAIIIIAAAggggAACCCCQ1AIEQJP64+HkEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAWAQKgseixLQIIIIAAAggggAACCCCAAAIIIIAAAggktQAB0KT+eDg5BBBAAAEEEEAAAQQQQAABBBBAAAEEEIhFgABoLHpsiwACCCCAAAIIIIAAAggggAACCCCAAAJJLUAANKk/Hk4OAQQQQAABBBBAAAEEEEAAAQQQQAABBGIRIAAaix7bIoAAAggggAACCCCAAAIIIIAAAggggEBSCxAATeqPh5NDAAEEEEAAAQQQQAABBBBAAAEEEEAAgVgECIDGose2CCCAAAIIIIAAAggggAACCCCAAAIIIJDUAgRAk/rj4eQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBYBAqCx6LEtAggggAACCCCAAAIIIIAAAggggAACCCS1AAHQpP54ODkEEEAAAQQQQAABBBBAAAEEEEAAAQQQiEWAAGgsemyLAAIIIIAAAggggAACCCCAAAIIIIAAAkktQAA0qT8eTg4BBBBAAAEEEEAAAQQQQAABBBBAAAEEYhEgABqLHtsigAACCCCAAAIIIIAAAggggAACCCCAQFILEABN6o+Hk0MAAQQQQAABBBBAAAEEEEAAAQQQQACBWAQIgMaix7YIIIAAAggggAACCCCAAAIIIIAAAgggkNQCBECT+uPh5BBAAAEEEEAAAQQQQAABBBBAAAEEEEAgFgECoLHosS0CCCCAAAIIIIAAAggggAACCCCAAAIIJLUAAdCk/ng4OQQQQAABBBBAAAEEEEAAAQQQQAABBBCIRYAAaCx6bIsAAggggAACCCCAAAIIIIAAAggggAACSS1AADSpPx5ODgEEEEAAAQQQQAABBBBAAAEEEEAAAQRiESAAGose2yKAAAIIIIAAAggggAACCCCAAAIIIIBAUgsUSeqz4+QQQAABBBAoYAIbN2609evXhyxVWlqalS9f3qpWrWqFCxcOmW/x4sW2b9++kOsjXVG3bl0rWbJkluwrVqywr776yhYuXGjp6el2wgknWOvWra1WrVou77vvvmtHHXWUtWjRIsu2LEAAAQQQSKyA97c61FGLFi3qvlfKlSsXKotbntvfLf4H37Jli33xxRe2atUq27Bhg/vuq1evnunl/920adMmmzBhgg0ZMsR/c+YRQAABBBCISiAt40dNelRbkBkBBBBAAAEEciwwc+ZMGzdunL3zzjvuB1+oHSn4eeSRR1qDBg3syiuvtPPPP9+KFPnfc8tq1arZ2rVrQ20e8fJvvvnG2rVr58t/4MABe/DBB+2uu+6yvXv32uGHH27Nmze3RYsWuR+pTZo0cUHP999/3x544AG74oorfNsygwACCCCQNwL6m/3111/b1KlTsz2BsmXLWo0aNezUU0+1G264werUqZMpf259t/gfZNq0aTZixAj3kO3gwYNulR6u6bx+/vln27Ztmylg27ZtW2vWrJlNnz7dFLidMWOG/26YRwABBBBAICoBmsBHxUVmBBBAAAEEYhNQsHHMmDE2b948K168uG9nl1xyial26PLly23ixIl27733utqW+uF38cUXu9ow+tHoJQUnlVQrc+zYsfbpp5/a/Pnz7ZdffrHbbrvNy2atWrVyy1Q7SDU6VYumX79+7selMnn78TbQtnpVrlzZpkyZYuvWrXP7XrlypX3++eeutuhLL73kfqCuXr3a24wpAggggEAeCgwfPtz9rb7mmmsyncV3333n/l7Pnj3bXnjhBbvsssvs999/t8cee8zq169v/fv3t/379/u28b4T4v3dogPs2LHDzj33XOvatav7PlHA03sYqFYHCnBu3brVND906FD3/vHHH7effvrJdu/e7TtHZhBAAAEEEMiRgGqAkhBAAAEEEEAg8QLHH3+8WmG41yOPPJLlBDJ+lKZfffXVvjwZNXfSv//+e5evRIkS6Z06dUrPqD2TZbvRo0f7tuncuXOW9VqQUcvT5fnoo49862fNmpVeqFAht3zSpEm+5f4zGc3u0zNqDrk8GbU//VcxjwACCCCQxwIZAUXf3/+M7lSCnk1GUDS9evXqvnwZD8XSDx065PLmxneLdpzR9Ut6RmsC3zEvvfTS9O3btwc9P29hRo1W33k2bNjQW8wUAQQQQACBHAlQAzRHYWM2QgABBBBAIHaBww47LNudqMn7E088Yd27d3f5du7caX379nV9cqqWzn333WcZActs9xFqpZo+VqpUKVMNUNXEyfgR7DYJ1benmiUqn/poW7NmTajdsxwBBBBAIA8Ewn2v6JTUp7P+jntJ3bKodUDGr0n3nRDv7xYdR99davmgpFqnakkQrj/SE0880d58803XHzY1QB0d/yCAAAIIxCCQs19NMRyQTRFAAAEEEEDgPwL+wUsNfhQqqa82L6l/NDVHV9+cbdq08RZHPVUfo2eccUamAKia0Hvps88+82azTNWHXEbNVKMJfBYaFiCAAAJ5KhDp94q6R6lQoYLvXDUYkR6s5cZ3y9tvv+2a5+tgpUqVspEjR/qOG25GQdDBgwfbnj17wmVlPQIIIIAAAtkKEADNloeVCCCAAAII5L2A/yBFOhsFQT/++OMc1/70SnT33XdbRhN57635B2HVV5xqA4VK6kd08+bNoVazHAEEEEAgiQUUKFU/n15S7cxixYrF/btl165drj9P7zi33HKLZTS/995GNL311ltNA/SREEAAAQQQiEWAAGgsemyLAAIIIIBAAgQ0IIR/yugLzVq2bOm/KEfzNWvWtKpVq/q2VbNIL2nApUGDBtlff/3lLco0VS2hcKMNZ9qANwgggAACSSXg/92ibk3UMiDe3y2PPvqorVq1ylfuIUOG+OYjndF3lQZxIiGAAAIIIBCLAAHQWPTYFgEEEEAAgQQIfPvtt76jqN/OOnXq+N7Hc+b666+3I444wrfLF1980f0Y1ijCwVKDBg2CLWYZAggggECSC2i09cWLF/vOMpYuVXw7CTLj//1VunRpq1KlSpBc4RdpxHoSAggggAACsQgQAI1Fj20RQAABBBDIZYE//vjD3n33Xd9R+vTp45uP94yCn6+88oqVKVPGt+uFCxda27Zt7ZprrrGMEXt9y5lBAAEEEMi/AqqZ6XVzor5ATz/99FwpzE8//eTbb+3atX3zzCCAAAIIIJBogSKJPiDHQwABBBBAAIHIBH799Vfr1q2bG/RIW5x//vn20EMPRbZxDnPpR7Bq7PTq1cuWLl3q9qKR4Z988kk3avAzzzxjPXv2zOHe2QwBBBBAIC8F9Pf8jjvusFGjRrnTUK3Mjz76KFdaFqj/T/9m9pG2XlCrA//tAr2OPfZYa9SoUeBi3iOAAAIIIJCtAAHQbHlYiQACCCCAQGIE3n//fVfDUj9ON23aZD/88IPNnDnT9F61ZjQK7k033eT6aMvtM9IPSx1/+PDhNnr0aDt48KA75Lp16+yss85yfYOq9pB+OJMQQAABBJJTYPfu3Xbbbbe5Wv2qwb969WrXd/OGDRusSJEi7mGW1mtE+NxIS5Ys8dUy1f5r1aoV0WHUF6m+b8aNG5ep78/y5cu71ggKgJIQQAABBBCIVoAAaLRi5EcAAQQQQCAXBNauXWtffPGFG+m2bNmydvzxx7smiS1atLDTTjst5hHfoz1lBTcffPBB69u3r1155ZXm3w/o888/70ainzx5sukHKQkBBBBAIPkE9ABt7ty5tn//fndyGvRuwIAB7qHa2WefHfVo7NGWsFSpUpk22bJlS6b3od7oe08vdfly5JFH+s7/73//u/3jH/8ItRnLEUAAAQQQyFaAAGi2PKxEAAEEEEAgMQJ/+9vfTIMQJVtq1qyZq4mqpveqEbp37153iqqdet555zESfLJ9YJwPAggg8F8B9ef8ySef5JmHgpf+yX/QJf/loeYrVqzoAqArV650WWj2HkqK5QgggAACkQgwCFIkSuRBAAEEEEAghQXUHPGWW26xr7/+OlONoWnTptnHH3+cwjIUHQEEEEAglIBaCPj3+6km8dGmcuXK+TYpUaKEb54ZBBBAAAEEohUgABqtGPkRQAABBBAogAIKZIZrntiyZUt76623MjXHnzp1agHUoEgIIIAAAvEQUFN7LwUOiuQtz26alpbmW+0/71vIDAIIIIAAAhEKEACNEIpsCCCAAAIIFGQB1ebUSO/hUvv27a1Lly6+bF7TRN8CZhBAAAEEEPivwKBBgzIN3jdixAhsEEAAAQQQyBMBAqB5ws5BEUAAAQQQSC6BevXq2RNPPOFGog93Zsccc4wvS8OGDX3zzCCAAAIIIOAv0LhxYxsyZIhv0QsvvGBz5szxvWcGAQQQQACBRAkQAE2UNMdBAAEEEEAgQOCvv/7yLTl48KBvPtaZnOxXAdANGzZENBDTihUrfKfYpk0b3zwzCCCAAAJ5K5CTv/+RnnFO9z1q1Chr1aqVO4xGpr/66qvtzz//jPSw5EMAAQQQQCAuAgRA48LIThBAAAEEEIheYPPmzb6NNm7c6JuPdWbTpk2+XUS637p167ptXn75ZffjdN++fb59+M8sXbrUpk+f7hZ169bNunfv7r+aeQQQQACBPBTw/17ZuXOn+QctYz2tnHy36Jhly5a1KVOm+IKgs2fPthYtWtjcuXOzPSU9GFy/fn22eViJAAIIIIBApAIEQCOVIh8CCCCAAAJxFPj+++/Nf0TcSZMm2e7du2M+ggaZmDhxom8/Osb8+fN970PN1KxZ04oXL+5Wjxkzxjp37mxffvllpnOaMWOGnXnmma7mjgKm48ePzzQgUqh9sxwBBBBAIPcFFDCcMGFCpgO9/fbbmd7n9E1Ov1u841WsWNG+/fZbe+aZZ6xy5cq2ePH/t3fHunTHYRiAv4OBRWKQGC0GiziJgcGEiAuQWF2AFffA4g6sBjEYXIJYTdJJLEZikhja/v5JG6cGIU1O8ubp0Go5rfd5v7TJ24MftbKyUoeHh9W+CN/7/6xrH41wdnZWGxsb3a+PjIzU9vZ2ra6u/vnt/EiAAAECBL4s0Pv5+9uXH+UBBAgQIECAwLcELi8v6/j4uK6vr+vt7W3g95iamqrl5eXa29urra2tgdd99pOrq6s6OTmpm5uben5+Hnjz0dHRah+q3r6A0dHR0cDr3v9kfn6+1tfXq31ez4uLi7/vY/ucn+1ZRY+PjzUxMVEHBwe1v7/fvfz+8V4mQIAAgeEI7OzsdH9nPzw8fHgH+v1+LSws1Onp6YfXffYL/+Pfln//jJeXl2pfDOn8/Lzu7++rfVh8+zYzM9M9Y/Xp6an7wklLS0u1ublZu7u7NTs7272N7wgQIECAwHcFDKDflfM4AgQIECAQJtCehbO2tlZjY2NdsvZsovYM0tvb2+r1etWG0Lm5uRofHw9LLg4BAgQIDEPg9fW1ezbo3d1d92zP6enpbghdXFysycnJYbxL/kwCBAgQCBUwgIYWKxYBAgQIECBAgAABAgQIECBAgAABAlU+B6grIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgQMoG6AAAECBAgQIECAAAECBAgQIECAAIFYAQNobLWCESBAgAABAgQIECBAgAABAgQIECBgAHUDBAgQIECAAAECBAgQIECAAAECBAjEChhAY6sVjAABAgQIECBAgAABAgQIECBAgAABA6gbIECAAAECBAgQIECAAAECBAgQIEAgVsAAGlutYAQIECBAgAABAgQIECBAgAABAgQIGEDdAAECBAgQIECAAAECBAgQIECAAAECsQIG0NhqBSNAgAABAgQIECBAgAABAgQIECBAwADqBggQIECAAAECBAgQIECAAAECBAgQiBUwgMZWKxgBAgQIECBAgAABAgQIECBAgAABAgZQN0CAAAECBAgQIECAAAECBAgQIECAQKyAATS2WsEIECBAgAABAgQIECBAgAABAgQIEDCAugECBAgQIECAAAECBAgQIECAAAECBGIFDKCx1QpGgAABAgQIECBAgAABAgQIECBAgIAB1A0QIECAAAECBAgQIECAAAECBAgQIBArYACNrVYwAgQIECBAgAABAgQIECBAgAABAgR+AdACkcEgb+J7AAAAAElFTkSuQmCC" width="672" /></p>
<pre class="r"><code>coefplot(type_vote4,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Time&#39;, &#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   sep = 0.5,
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, 
         col = 1, pch = c(20, 17),
       cex = 0.7,
       legend = c(&#39;2016 Voting&#39;, &#39;2020 Voting&#39;),
       title = &#39;Turnout DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#### Analysis by Study

m_trst2 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, study == &#39;Two&#39;))</code></pre>
<pre><code>## NOTE: 310 observations removed because of NA values (RHS: 310).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst2)</code></pre>
<pre><code>## Standard-errors: IID 
## Dep. var.: trstgov
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS          0.004479   0.045636  0.098140 9.2185e-01    
## PTG          0.037030   0.043870  0.844067 3.9894e-01    
## Time        -0.035426   0.032938 -1.075543 2.8252e-01    
## Black        0.065296   0.038210  1.708848 8.7946e-02 .  
## Female      -0.023484   0.024589 -0.955056 3.3990e-01    
## Class        0.018435   0.032443  0.568235 5.7007e-01    
## Republican  -0.171834   0.026733 -6.427760 2.4734e-10 ***
## PolInterest  0.063940   0.037635  1.698969 8.9793e-02 .  
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.107486   0.048666 -2.208621 0.0275388 *  
## PTG          0.057683   0.046782  1.233012 0.2180038    
## Time        -0.035454   0.034895 -1.016025 0.3099841    
## Black        0.040769   0.040615  1.003796 0.3158393    
## Female       0.001813   0.026181  0.069257 0.9448057    
## Class        0.012724   0.034612  0.367605 0.7132836    
## Republican  -0.035590   0.028640 -1.242674 0.2144229    
## PolInterest  0.113411   0.040230  2.819096 0.0049581 ** 
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.070899   0.049316 -1.437634 0.1510077    
## PTG          0.040306   0.047287  0.852352 0.3943252    
## Time        -0.021360   0.035356 -0.604144 0.5459536    
## Black        0.007763   0.041609  0.186581 0.8520464    
## Female       0.002528   0.026579  0.095128 0.9242416    
## Class        0.077833   0.035119  2.216285 0.0270092 *  
## Republican  -0.010563   0.029090 -0.363103 0.7166429    
## PolInterest  0.116289   0.040921  2.841787 0.0046234 ** 
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.146891   0.070747 -2.076285 3.8242e-02 *  
## PTG          0.008623   0.068141  0.126554 8.9933e-01    
## Time        -0.087724   0.050908 -1.723188 8.5309e-02 .  
## Black       -0.070950   0.059684 -1.188757 2.3495e-01    
## Female      -0.068070   0.038389 -1.773181 7.6646e-02 .  
## Class        0.259725   0.050669  5.125897 3.8627e-07 ***
## Republican  -0.031458   0.041801 -0.752559 4.5198e-01    
## PolInterest  0.155612   0.058986  2.638110 8.5274e-03 ** 
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.249379   0.045745 -5.451450 7.0161e-08 ***
## PTG          0.043994   0.044095  0.997700 3.1878e-01    
## Time        -0.029395   0.032890 -0.893734 3.7178e-01    
## Black       -0.060921   0.038875 -1.567106 1.1756e-01    
## Female       0.001178   0.024662  0.047763 9.6192e-01    
## Class        0.174834   0.032690  5.348174 1.2182e-07 ***
## Republican  -0.032207   0.026922 -1.196275 2.3201e-01    
## PolInterest  0.089551   0.038056  2.353152 1.8901e-02 *</code></pre>
<pre class="r"><code>m_trst3 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, study == &#39;Three&#39;))</code></pre>
<pre><code>## NOTE: 360 observations removed because of NA values (RHS: 360).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst3)</code></pre>
<pre><code>## Standard-errors: IID 
## Dep. var.: trstgov
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS          0.176567   0.051762  3.411097 0.00068929 ***
## PTG         -0.041344   0.053854 -0.767694 0.44296243    
## Time        -0.031966   0.037750 -0.846773 0.39745011    
## Black        0.097047   0.045716  2.122823 0.03416627 *  
## Female      -0.016859   0.027130 -0.621422 0.53455160    
## Class        0.013320   0.037876  0.351673 0.72520348    
## Republican  -0.064892   0.066628 -0.973941 0.33046732    
## PolInterest  0.008340   0.046830  0.178089 0.85871122    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.069723   0.050864  1.370782 0.170937    
## PTG          0.079361   0.052923  1.499560 0.134236    
## Time        -0.006882   0.036924 -0.186371 0.852215    
## Black        0.006265   0.043732  0.143271 0.886123    
## Female      -0.081597   0.026665 -3.060082 0.002308 ** 
## Class        0.065115   0.037016  1.759092 0.079054 .  
## Republican   0.079518   0.065603  1.212112 0.225930    
## PolInterest -0.038211   0.045627 -0.837465 0.402653    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.019568   0.049477 -0.395502 0.6926081    
## PTG          0.078758   0.051316  1.534775 0.1253477    
## Time        -0.006870   0.035660 -0.192656 0.8472911    
## Black        0.011239   0.042359  0.265333 0.7908409    
## Female      -0.029905   0.025759 -1.160921 0.2461192    
## Class        0.117147   0.036085  3.246408 0.0012315 ** 
## Republican   0.026547   0.063701  0.416745 0.6770086    
## PolInterest  0.058485   0.044093  1.326380 0.1852006    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.109722   0.074181 -1.479113 1.3961e-01    
## PTG          0.122681   0.077324  1.586591 1.1311e-01    
## Time        -0.136023   0.053556 -2.539837 1.1329e-02 *  
## Black       -0.205306   0.063914 -3.212226 1.3841e-03 ** 
## Female      -0.160374   0.038789 -4.134537 4.0376e-05 ***
## Class        0.063918   0.054142  1.180559 2.3822e-01    
## Republican  -0.045654   0.096529 -0.472957 6.3641e-01    
## PolInterest  0.044621   0.066554  0.670451 5.0282e-01    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.189793   0.045575 -4.164400 3.5620e-05 ***
## PTG          0.203184   0.047119  4.312181 1.8790e-05 ***
## Time        -0.035382   0.032848 -1.077147 2.8183e-01    
## Black       -0.088748   0.038809 -2.286823 2.2541e-02 *  
## Female       0.013397   0.023620  0.567186 5.7079e-01    
## Class        0.054129   0.033266  1.627136 1.0421e-01    
## Republican  -0.033178   0.058292 -0.569180 5.6944e-01    
## PolInterest  0.177142   0.040586  4.364654 1.4904e-05 ***</code></pre>
<pre class="r"><code>m_trst4 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, study == &#39;Four&#39;))</code></pre>
<pre><code>## NOTE: 333 observations removed because of NA values (RHS: 333).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst4)</code></pre>
<pre><code>## Standard-errors: IID 
## Dep. var.: trstgov
##              Estimate Std. Error    t value  Pr(&gt;|t|)    
## PTS          0.102370   0.049268   2.077824 0.0381293 *  
## PTG          0.109156   0.043134   2.530601 0.0116287 *  
## Time        -0.072335   0.033834  -2.137927 0.0329062 *  
## Black        0.018580   0.040892   0.454383 0.6497093    
## Female      -0.058766   0.024455  -2.403004 0.0165487 *  
## Class        0.105422   0.034197   3.082837 0.0021398 ** 
## Republican  -0.280775   0.025041 -11.212598 &lt; 2.2e-16 ***
## PolInterest  0.002569   0.038768   0.066270 0.9471837    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS          0.028058   0.050145  0.559530 5.7600e-01    
## PTG          0.037927   0.043916  0.863624 3.8812e-01    
## Time        -0.093503   0.034363 -2.721016 6.6859e-03 ** 
## Black       -0.018699   0.041265 -0.453137 6.5060e-01    
## Female      -0.053451   0.024912 -2.145609 3.2281e-02 *  
## Class        0.070059   0.034859  2.009765 4.4877e-02 *  
## Republican  -0.106076   0.025502 -4.159489 3.6271e-05 ***
## PolInterest  0.024297   0.039291  0.618383 5.3654e-01    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.024579   0.049997 -0.491616 6.2316e-01    
## PTG          0.022437   0.043876  0.511378 6.0926e-01    
## Time        -0.018760   0.034256 -0.547627 5.8414e-01    
## Black        0.023113   0.040640  0.568743 5.6973e-01    
## Female      -0.077527   0.024785 -3.128034 1.8408e-03 ** 
## Class        0.081141   0.034837  2.329147 2.0166e-02 *  
## Republican  -0.111248   0.025377 -4.383851 1.3658e-05 ***
## PolInterest  0.138133   0.038792  3.560908 3.9739e-04 ***
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.264081   0.076545 -3.450030 0.00059633 ***
## PTG         -0.046826   0.068333 -0.685269 0.49341531    
## Time        -0.108860   0.053053 -2.051920 0.04057264 *  
## Black       -0.139503   0.063043 -2.212836 0.02725141 *  
## Female       0.008673   0.038582  0.224780 0.82222026    
## Class        0.129739   0.054120  2.397268 0.01679569 *  
## Republican  -0.153704   0.039346 -3.906465 0.00010334 ***
## PolInterest  0.162075   0.061027  2.655796 0.00810378 ** 
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.232758   0.047936 -4.855596 1.5071e-06 ***
## PTG          0.081957   0.042312  1.936972 5.3184e-02 .  
## Time        -0.001697   0.032851 -0.051660 9.5882e-01    
## Black       -0.076205   0.039199 -1.944059 5.2322e-02 .  
## Female      -0.058412   0.023834 -2.450759 1.4520e-02 *  
## Class        0.137232   0.033534  4.092271 4.8126e-05 ***
## Republican  -0.124913   0.024332 -5.133759 3.7632e-07 ***
## PolInterest  0.056868   0.037808  1.504142 1.3303e-01</code></pre>
<pre class="r"><code>m_trst5 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ PTS + PTG + Time +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, study == &#39;Five&#39;))</code></pre>
<pre><code>## NOTE: 290 observations removed because of NA values (RHS: 290).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_trst5)</code></pre>
<pre><code>## Standard-errors: IID 
## Dep. var.: trstgov
##              Estimate Std. Error    t value   Pr(&gt;|t|)    
## PTS          0.013487   0.044649   0.302068 0.76269119    
## PTG          0.062561   0.041945   1.491515 0.13628481    
## Time        -0.095968   0.031167  -3.079145 0.00215856 ** 
## Black        0.059021   0.034702   1.700823 0.08942820 .  
## Female      -0.047240   0.022283  -2.120014 0.03436220 *  
## Class        0.114596   0.030942   3.703503 0.00022969 ***
## Republican  -0.336424   0.022802 -14.753949  &lt; 2.2e-16 ***
## PolInterest -0.072738   0.037773  -1.925678 0.05455639 .  
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.074339   0.049341 -1.506629 1.3237e-01    
## PTG          0.050758   0.047305  1.073010 2.8364e-01    
## Time        -0.067276   0.034881 -1.928728 5.4176e-02 .  
## Black        0.045983   0.037904  1.213167 2.2548e-01    
## Female       0.015655   0.025047  0.625027 5.3216e-01    
## Class        0.131067   0.034613  3.786623 1.6607e-04 ***
## Republican  -0.139296   0.025548 -5.452276 6.9414e-08 ***
## PolInterest -0.058933   0.042008 -1.402892 1.6110e-01    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.180399   0.048452 -3.723271 2.1277e-04 ***
## PTG          0.053159   0.046252  1.149325 2.5082e-01    
## Time        -0.081569   0.034322 -2.376572 1.7747e-02 *  
## Black       -0.057256   0.037710 -1.518296 1.2940e-01    
## Female      -0.028802   0.024568 -1.172356 2.4146e-01    
## Class        0.069223   0.033973  2.037615 4.1971e-02 *  
## Republican  -0.141773   0.024975 -5.676520 2.0284e-08 ***
## PolInterest  0.008122   0.041507  0.195689 8.4491e-01    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.183175   0.074743 -2.450731 0.01449991 *  
## PTG          0.072243   0.071870  1.005191 0.31515158    
## Time        -0.152619   0.052388 -2.913240 0.00369081 ** 
## Black       -0.030005   0.057001 -0.526397 0.59877900    
## Female      -0.056097   0.037838 -1.482552 0.13864291    
## Class        0.179310   0.052412  3.421160 0.00065972 ***
## Republican  -0.103840   0.038828 -2.674325 0.00766248 ** 
## PolInterest  0.066553   0.063921  1.041180 0.29815087    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS         -0.224728   0.047398 -4.741278 2.5781e-06 ***
## PTG          0.108098   0.045346  2.383842 1.7401e-02 *  
## Time        -0.104236   0.033204 -3.139241 1.7657e-03 ** 
## Black       -0.085928   0.035918 -2.392322 1.7007e-02 *  
## Female       0.015876   0.023931  0.663427 5.0728e-01    
## Class        0.113945   0.033226  3.429356 6.4080e-04 ***
## Republican  -0.050001   0.024530 -2.038314 4.1897e-02 *  
## PolInterest  0.048165   0.040237  1.197035 2.3170e-01</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst2[1],
                           &#39;State&#39;  = m_trst2[2],
                           &#39;Local&#39;  = m_trst2[3],
                           &#39;Interpersonal I&#39;  = m_trst2[4],
                           &#39;Interpersonal II&#39;  = m_trst2[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst3[1],
                           &#39;State&#39;  = m_trst3[2],
                           &#39;Local&#39;  = m_trst3[3],
                           &#39;Interpersonal I&#39;  = m_trst3[4],
                           &#39;Interpersonal II&#39;  = m_trst3[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst4[1],
                           &#39;State&#39;  = m_trst4[2],
                           &#39;Local&#39;  = m_trst4[3],
                           &#39;Interpersonal I&#39;  = m_trst4[4],
                           &#39;Interpersonal II&#39;  = m_trst4[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_trst5[1],
                           &#39;State&#39;  = m_trst5[2],
                           &#39;Local&#39;  = m_trst5[3],
                           &#39;Interpersonal I&#39;  = m_trst5[4],
                           &#39;Interpersonal II&#39;  = m_trst5[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>### Duration Effects

m_time1 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Time2 == 1))</code></pre>
<pre><code>## NOTE: 26 observations removed because of NA values (RHS: 26).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_time1)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.001696   0.165396 -0.010256  0.99246    
## PTG          0.082201   0.119490  0.687931  0.54091    
## Black        0.094368   0.058029  1.626219  0.20238    
## Female      -0.081201   0.059212 -1.371356  0.26384    
## Class        0.057096   0.034080  1.675316  0.19247    
## Republican  -0.310680   0.065182 -4.766375  0.01754 *  
## PolInterest  0.040448   0.105828  0.382207  0.72778    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.154685   0.119324  1.296341 0.285570    
## PTG          0.227494   0.109743  2.072973 0.129872    
## Black       -0.011540   0.052929 -0.218019 0.841402    
## Female      -0.101140   0.029249 -3.457914 0.040702 *  
## Class        0.065507   0.021796  3.005461 0.057419 .  
## Republican  -0.171037   0.032971 -5.187505 0.013911 *  
## PolInterest  0.124961   0.058666  2.130055 0.123005    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.042908   0.068190  0.629246 0.573843    
## PTG          0.066386   0.069165  0.959818 0.407953    
## Black        0.000271   0.053813  0.005031 0.996302    
## Female      -0.100097   0.073695 -1.358265 0.267499    
## Class        0.041148   0.084297  0.488135 0.658907    
## Republican  -0.144024   0.051297 -2.807682 0.067422 .  
## PolInterest -0.044587   0.039971 -1.115482 0.345951    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.120400   0.133129  0.904387 0.432456    
## PTG          0.141006   0.183129  0.769982 0.497386    
## Black       -0.105290   0.130684 -0.805683 0.479368    
## Female      -0.172693   0.095546 -1.807437 0.168422    
## Class        0.052915   0.134167  0.394396 0.719653    
## Republican  -0.252459   0.048317 -5.225072 0.013636 *  
## PolInterest  0.162894   0.209082  0.779092 0.492735    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.057768   0.114676 -0.503751 0.649101    
## PTG          0.127325   0.078823  1.615328 0.204653    
## Black       -0.025939   0.121715 -0.213110 0.844902    
## Female      -0.033455   0.030930 -1.081623 0.358609    
## Class        0.112184   0.046099  2.433532 0.093036 .  
## Republican  -0.111165   0.049699 -2.236740 0.111298    
## PolInterest  0.195013   0.039363  4.954267 0.015785 *</code></pre>
<pre class="r"><code>m_time2 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Time2 == 2))</code></pre>
<pre><code>## NOTE: 47 observations removed because of NA values (RHS: 47).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_time2)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value   Pr(&gt;|t|)    
## PTS          0.290733   0.020539 14.154984 0.00076383 ***
## PTG         -0.047450   0.061753 -0.768387 0.49820387    
## Black        0.018648   0.085481  0.218158 0.84130326    
## Female      -0.101157   0.026914 -3.758500 0.03292385 *  
## Class        0.242299   0.041828  5.792739 0.01023489 *  
## Republican  -0.192324   0.050459 -3.811510 0.03175548 *  
## PolInterest -0.211736   0.134732 -1.571530 0.21409861    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS          0.198311   0.024188  8.198619 0.0037972 ** 
## PTG         -0.142136   0.112625 -1.262035 0.2961297    
## Black        0.071993   0.083221  0.865084 0.4506279    
## Female      -0.059219   0.035772 -1.655441 0.1964106    
## Class        0.162815   0.039225  4.150847 0.0254110 *  
## Republican  -0.052486   0.021963 -2.389769 0.0967615 .  
## PolInterest -0.112973   0.151594 -0.745231 0.5102055    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.157352   0.086781  1.813199 0.167454    
## PTG         -0.101610   0.136751 -0.743024 0.511362    
## Black       -0.006397   0.063893 -0.100115 0.926568    
## Female      -0.042393   0.016521 -2.566051 0.082777 .  
## Class        0.216417   0.049844  4.341875 0.022550 *  
## Republican  -0.040630   0.017447 -2.328756 0.102264    
## PolInterest  0.002450   0.125488  0.019526 0.985648    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS          0.113940   0.050615  2.251109 0.1098253    
## PTG         -0.370174   0.079799 -4.638851 0.0188785 *  
## Black       -0.063269   0.083517 -0.757564 0.5037840    
## Female      -0.139048   0.083062 -1.674032 0.1927179    
## Class        0.410987   0.048316  8.506303 0.0034123 ** 
## Republican  -0.148740   0.065111 -2.284402 0.1065030    
## PolInterest -0.000635   0.077153 -0.008228 0.9939517    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.151697   0.064554 -2.349939 0.100311    
## PTG         -0.055266   0.090428 -0.611154 0.584291    
## Black       -0.026538   0.037499 -0.707696 0.530160    
## Female      -0.027948   0.025066 -1.114972 0.346138    
## Class        0.252042   0.043306  5.819973 0.010101 *  
## Republican  -0.014015   0.028995 -0.483354 0.661927    
## PolInterest  0.127272   0.062602  2.033034 0.134949</code></pre>
<pre class="r"><code>m_time3 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Time2 == 3))</code></pre>
<pre><code>## NOTE: 73 observations removed because of NA values (RHS: 73).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_time3)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.049364   0.054073 -0.912921 0.4285979    
## PTG          0.037567   0.091619  0.410032 0.7093057    
## Black        0.104463   0.034139  3.059964 0.0549946 .  
## Female      -0.074977   0.010841 -6.915897 0.0061968 ** 
## Class       -0.000198   0.041870 -0.004737 0.9965180    
## Republican  -0.245360   0.036697 -6.686119 0.0068239 ** 
## PolInterest  0.038862   0.056391  0.689152 0.5402449    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.234512   0.027125 -8.645583 0.003255 ** 
## PTG          0.083451   0.089278  0.934735 0.418879    
## Black       -0.022238   0.045573 -0.487960 0.659017    
## Female      -0.044001   0.043506 -1.011386 0.386321    
## Class        0.013855   0.020574  0.673426 0.548915    
## Republican  -0.077211   0.080840 -0.955104 0.409986    
## PolInterest -0.011663   0.079416 -0.146861 0.892556    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.210903   0.036238 -5.819947 0.010101 *  
## PTG          0.120792   0.093199  1.296064 0.285654    
## Black        0.003345   0.039312  0.085087 0.937553    
## Female      -0.071853   0.023228 -3.093410 0.053571 .  
## Class       -0.008563   0.048969 -0.174864 0.872322    
## Republican  -0.047622   0.087293 -0.545546 0.623326    
## PolInterest  0.144206   0.057153  2.523156 0.085938 .  
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.296023   0.048336 -6.124235 0.0087524 ** 
## PTG          0.076803   0.064706  1.186942 0.3206755    
## Black       -0.043768   0.124991 -0.350173 0.7493517    
## Female      -0.068517   0.050043 -1.369152 0.2644489    
## Class        0.161340   0.027374  5.893869 0.0097498 ** 
## Republican  -0.136110   0.119224 -1.141629 0.3364790    
## PolInterest -0.037902   0.055827 -0.678915 0.5458764    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.378265   0.067729 -5.584981 0.011335 *  
## PTG          0.124462   0.056559  2.200568 0.115111    
## Black       -0.106458   0.030945 -3.440187 0.041230 *  
## Female       0.024269   0.043879  0.553087 0.618749    
## Class        0.059161   0.047874  1.235757 0.304492    
## Republican  -0.067725   0.057140 -1.185246 0.321253    
## PolInterest -0.006214   0.072991 -0.085136 0.937517</code></pre>
<pre class="r"><code>m_time4 &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS + PTG +
                  Black + Female + Class + Republican + PolInterest | study,
                weights = ~weight,
                data = subset(datp, Time2 == 4))</code></pre>
<pre><code>## NOTE: 169 observations removed because of NA values (RHS: 169).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_time4)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.100010   0.073296  1.364461 0.265759    
## PTG          0.032572   0.064315  0.506449 0.647417    
## Black        0.055014   0.051993  1.058101 0.367668    
## Female       0.007844   0.008976  0.873961 0.446465    
## Class        0.038485   0.030722  1.252684 0.299078    
## Republican  -0.263995   0.052713 -5.008171 0.015324 *  
## PolInterest  0.046697   0.022124  2.110682 0.125286    
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.025331   0.065380 -0.387447 0.724279    
## PTG          0.041300   0.043907  0.940632 0.416286    
## Black        0.030148   0.032707  0.921774 0.424629    
## Female       0.004195   0.013590  0.308683 0.777757    
## Class        0.070012   0.035568  1.968416 0.143668    
## Republican  -0.089103   0.036269 -2.456745 0.091131 .  
## PolInterest  0.035683   0.033610  1.061686 0.366274    
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.096364   0.057477 -1.676568 0.192220    
## PTG          0.042363   0.036256  1.168447 0.327033    
## Black        0.001145   0.035574  0.032200 0.976335    
## Female      -0.000416   0.015835 -0.026266 0.980695    
## Class        0.092048   0.021635  4.254591 0.023803 *  
## Republican  -0.094255   0.039687 -2.374975 0.098062 .  
## PolInterest  0.099546   0.042542  2.339925 0.101229    
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS         -0.258906   0.079976 -3.237280 0.047950 *  
## PTG          0.057707   0.033879  1.703318 0.187059    
## Black       -0.144644   0.074551 -1.940205 0.147682    
## Female      -0.016107   0.050478 -0.319088 0.770587    
## Class        0.122574   0.054561  2.246566 0.110288    
## Republican  -0.054970   0.046704 -1.177005 0.324076    
## PolInterest  0.174795   0.056715  3.081963 0.054053 .  
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.213317   0.029080 -7.335410 0.0052345 ** 
## PTG          0.113745   0.053962  2.107880 0.1256199    
## Black       -0.105929   0.052932 -2.001226 0.1391605    
## Female      -0.006996   0.021179 -0.330303 0.7628928    
## Class        0.113293   0.039985  2.833350 0.0660071 .  
## Republican  -0.082716   0.041768 -1.980353 0.1420087    
## PolInterest  0.092427   0.026468  3.492004 0.0397093 *</code></pre>
<pre class="r"><code># modelsummary(models = list(&#39;Federal&#39;  = m_trst5[1],
#                            &#39;State&#39;  = m_trst5[2],
#                            &#39;Local&#39;  = m_trst5[3],
#                            &#39;Interpersonal I&#39;  = m_trst5[4],
#                            &#39;Interpersonal II&#39;  = m_trst5[5]),
#              output = &#39;latex&#39;,
#              stars = c(&#39;*&#39; = .05),
#              gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
#              escape = FALSE)

coefplot(m_time1,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAVAoAMABAAAAAEAAAPAAAAAALYRw1EAAEAASURBVHgB7N0JuBxVnSjwk4TskABJIJCQBEgAEQmQsBkWBxGi8hQFBBF4iMOi46AybnzqE8RRZ+aNCOIIMyOKIKNvXMEB2R8MywhhMQuQhdWEkI2EJGQnef7LV5ksN0nfe6vv7ar+1fd16t7u6lP/8zsndav/XXVOl3V/WpKFAAECBAgQIECAAAECBAgQIECAAAECFRToWsE6qRIBAgQIECBAgAABAgQIECBAgAABAgQyAQlQHYEAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAAEJUH2AAAECBAgQIECAAAECBAgQIECAAIHKCkiAVrZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQlQfYAAAQIECBAgQIAAAQIECBAgQIAAgcoKSIBWtmlVjAABAgQIECBAgAABAgQIECBAgAABCVB9gAABAgQIECBAgAABAgQIECBAgACBygpIgFa2aVWMAAECBAgQIECAAAECBAgQIECAAIHtEBDoSIGXX345XXPNNWn16tUduVv7IkCAAAECBAgQIECAAAECBAiUUqB///7pc5/7XOrbt28p42+EoCVAG6EVmiiGSH7+wz/8QxPVWFUJECBAgAABAgQIECBAgAABAu0TeMtb3pJOP/309hXSxO+WAG3ixu+MqudXfp588snp2GOP7YwQ7JMAAQIECBAgQIAAAQIECBAgUAqBm266KT3++OPupG1na0mAthPQ29smEMnPT3/60217s3cRIECAAAECBAgQIECAAAECBJpAIJKf8bC0T8AkSO3z824CBAgQIECAAAECBAgQIECAAAECBBpYQAK0gRtHaAQIECBAgAABAgQIECBAgAABAgQItE9AArR9ft5NgAABAgQIECBAgAABAgQIECBAgEADC0iANnDjCK19ArNnz04PPPBAeuONN9pXkHcT6GCBlStXpgcffDC9/PLLHbxnuyPQfoFJkyalCRMmtL8gJRDoYIE5c+Zk5w1Lly7t4D3bHYH2CaxatSo7b3jxxRfbV5B3E+gEgSlTpqRHH300rVu3rhP2bpcE2i4wd+7c7Lxh8eLFbS/EOztUQAK0Q7ntrCMFIgG6cOHCtGDBgo7crX0RaLfAokWLsn47a9asdpelAAIdLRCJ++i7q1ev7uhd2x+Bdgnk5w3z589vVzneTKCjBV5//XXnDR2Nbn+FCcR5Qxx/I5FvIVAmgVdffTXLNzhvKE+rSYCWp61ESoAAAQIECBAgQIAAAQIECBAgQIBAKwUkQFsJZnMCBAgQIECAAAECBAgQIECAAIHmFejSpUvzVr6kNd+upHELm8A2BXbfffds/M8BAwZsc1sbEGgkgR133DENHDgwDRkypJHCEguBmgSGDx+eYhzb7t2717S9jQg0ikCcNyxZsiQ7/jZKTOIgUItA//79s347dOjQWja3DYGGEojzhpizoUePHg0Vl2AIbEtg8ODBKYYuGzRo0LY29XqDCEiANkhDCKN4gTggxcNCoGwCPXv2TOPGjStb2OIlkAkccMABJAiUUmCXXXZJ8bAQKJtAJI6cN5St1cSbC+y///75j9YESiUQiU/Jz1I1WXILfLnaS7QECBAgQIAAAQIECBAgQIAAAQIECLRCQAK0FVg2JUCAAAECBAgQIECAAAECBAgQIECgXAISoOVqL9ESIECAAAECBAgQIECAAAECBAgQINAKAQnQVmDZlAABAgQIECBAgAABAgQIECBAgACBcglIgJarvUTbCoF169al5cuXt+IdNiXQOAIrVqxIa9eubZyAREKgRoE1a9akVatW1bi1zQg0joDzhsZpC5G0XsB5Q+vNvKMxBJw3NEY7iKJtAvINbXPrrHdJgHaWvP3WXeDZZ59Nd955Z5o3b17d92UHBIoUWLx4cbrjjjvSxIkTiyxWWQQ6ROCBBx5I99xzjwR+h2jbSZEC06ZNy84b5syZU2SxyiJQd4ElS5Zk5w1PPfVU3fdlBwSKFnjooYfS3Xffnd58882ii1YegboKzJgxIztvmD17dl33o/DiBCRAi7NUUoMJ5N/G5OsGC084BLYokPfZfL3FDb1AoAEFot/GFaA+yDRg4whpqwL5MTdfb3VjLxJoIIG4+jMWfbeBGkUoNQssW7YsrV69OsWVoBYCZRKIvhuLY295Wk0CtDxtJVICBAgQIECAAAECBAgQIECAAAECBFopIAHaSjCbl0ega9c/d+9u3bqVJ2iREviTQN538zUUAmUSyPttvi5T7GJtboG8z+br5tZQ+zIJ5Oe6+m6ZWk2suUDeb/N1/rw1gUYXyPtsvm70eMWX0nYQCFRVYJ999kn9+vVLgwcPrmoV1auiAgMHDkwHHnhgirWFQNkExo4dm93Gln8gL1v84m1egVGjRqW+ffumIUOGNC+CmpdSYKeddkqjR49OO++8cynj3zTo+fPnbzaO9MqVK1P37t3Xf0mcv6d3795phx12yH+1LqHAmDFjUt6+JQxfyE0sMHLkyBTHoKFDhzaxQrmqLgFarvYSbSsE+vTpk/baa69WvMOmBBpDoEuXLmnPPfdsjGBEQaCVAoMGDWrlO2xOoDEE4kPM3nvv3RjBiIJAKwTivGHEiBGteEfjbvqVr3wlff3rX685wPiy7f7770/jxo2r+T02bCwBX/g3VnuIpnaBXr16OW+onashtpQAbYhmEAQBAgQIECBAgAABAgSaW2DYsGFpt91222givZgc57XXXkuR7BwwYMBGQHH1Z1wBayFAgAABAtsSkADdlpDXCRAgQIAAAQIECBAgQKDuAueff36Kx4bL9OnTUwxtFXd2TZs2bcOX/EyAAAECBGoWMAlSzVQ2JECAAAECBAgQIECAAAECBAgQIECgbAISoGVrMfESIECAAAECBAgQIECAAAECBAgQIFCzgARozVQ2LJvAsmXL0nPPPbfRGEJlq4N4m1Ng3bp16fnnn09LlixpTgC1LrXA3Llz0yuvvFLqOgi+OQWWL1+eZsyYkWK8QQuBMgnEecMLL7yQFi9eXKawxUogE5g3b16aNWsWDQKlE3DeULomSxKg5WszEdcoEGMETZ48Oc2ePbvGd9iMQGMIzJ8/P02aNCk9/fTTjRGQKAi0QuDxxx9Pjz32mC+fWmFm08YQiHEGp0yZIoHfGM0hilYIxARBEydOzPpvK95mUwINIfDEE0+kCRMmpNWrVzdEPIIgUKtAXGwV5w0zZ86s9S2262QBCdBObgC7r5/A2rVrs8Lzdf32pGQCxQrkfTZfF1u60gjUVyDvt/m6vntTOoHiBPI+m6+LK1lJBOorkPfZfF3fvSmdQLECeb/N18WWrjQC9RPI+2y+rt+elFyUgARoUZLKIUCAAAECBAgQIECAAAECBAgQIECg4QS2a7iIBESgIIE+ffpkJeXrgopVDIG6C/Tu3Tvbh75bd2o7qINA9NsVK1akbt261aF0RRKon0B+zM3X9duTkgkUKxDnDV26dEn6brGuSusYgei3MY7tdttJTXSMuL0UJZAfc/N1UeUqp34CjjL1s1VyJwvsu+++acSIEalXr16dHIndE2idQL9+/dL48eNTjx49WvdGWxNoAIGjjz46+yDTtaubTBqgOYTQCoFRo0alYcOGOW9ohZlNG0Ng++23TyeeeGLq3r17YwQkCgKtEBg3blx23uCL01ag2bQhBEaOHJmGDh3qvKEhWqO2ICRAa3OyVQkF4ptwyc8SNpyQM4GePXuSIFBKAVdwlLLZBP0nAecNukGZBZw3lLn1mjt25w3N3f5lr718Q7la0OUZ5Wov0RIgQIAAAQIECBAgQIAAAQIECBAg0AoBCdBWYNmUAAECBAgQIECAAAECBAgQIECAAIFyCUiAlqu9REuAAAECBAgQIECAAAECBAgQIECAQCsEJEBbgWVTAgQIECBAgAABAgQIECBAgAABAgTKJSABWq72Em0rBGbPnp0eeOCB9MYbb7TiXTYl0PkCK1euTA8++GB6+eWXOz8YERBopcCkSZPShAkTWvkumxPofIE5c+Zk5w1Lly7t/GBEQKAVAqtWrcrOG1588cVWvMumBBpDYMqUKenRRx/NZoJvjIhEQaA2gblz52bnDYsXL67tDbbqdAEJ0E5vAgHUSyASoAsXLkwLFiyo1y6US6AuAosWLcr67axZs+pSvkIJ1FMgEvfRd1evXl3P3SibQOEC+XnD/PnzCy9bgQTqKfD66687b6gnsLLrKhDnDXH8jUS+hUCZBF599dUs3+C8oTytJgFanrYSKQECBAgQIECAAAECBAgQIECAAAECrRSQAG0lmM0JECBAgAABAgQIECBAgAABAgSaV6BLly7NW/mS1ny7ksYtbALbFNh9992z8T8HDBiwzW1tQKCRBHbcccc0cODANGTIkEYKSywEahIYPnx4inFsu3fvXtP2NiLQKAJx3rBkyZLs+NsoMYmDQC0C/fv3z/rt0KFDa9ncNgQaSiDOG2LOhh49ejRUXIIhsC2BwYMHpxi6bNCgQdva1OsNIiAB2iANIYziBeKAFA8LgbIJ9OzZM40bN65sYYuXQCZwwAEHkCBQSoFddtklxcNCoGwCkThy3lC2VhNvLrD//vvnP1oTKJVAJD4lP0vVZMkt8OVqL9ESIECAAAECBAgQIECAAAECBAgQINAKAQnQVmDZlAABAgQIECBAgAABAgQIECBAgACBcglIgJarvURLgAABAgQIECBAgAABAgQIECBAgEArBCRAW4FlUwIECBAgQIAAAQIECBAgQIAAAQIEyiUgAVqu9hJtKwTWrVuXli9f3op32JRA4wisWLEirV27tnECEgmBGgXWrFmTVq1aVePWNiPQOALOGxqnLUTSegHnDa03847GEHDe0BjtIIq2Ccg3tM2ts94lAdpZ8vZbd4Fnn3023XnnnWnevHl135cdEChSYPHixemOO+5IEydOLLJYZRHoEIEHHngg3XPPPRL4HaJtJ0UKTJs2LTtvmDNnTpHFKotA3QWWLFmSnTc89dRTdd+XHRAoWuChhx5Kd999d3rzzTeLLlp5BOoqMGPGjOy8Yfbs2XXdj8KLE5AALc5SSQ0mkH8bk68bLDzhENiiQN5n8/UWN/QCgQYUiH4bV4D6INOAjSOkrQrkx9x8vdWNvUiggQTi6s9Y9N0GahSh1CywbNmytHr16hRXgloIlEkg+m4sjr3labWGSYDKmpen04iUAAECBAgQIECAAAECBAgQIECAQFkEGiIBunTp0vTP//zPZTETZ0kEunb9c/fu1q1bSSIWJoE/C+R9N19zIVAmgbzf5usyxS7W5hbI+2y+bm4NtS+TQH6uq++WqdXEmgvk/TZf589bE2h0gbzP5utGj1d8KW3XCAg333xzioHnLQSKFNhnn31Sv3790uDBg4ssVlkE6i4wcODAdOCBB6ZYWwiUTWDs2LHZbWz5B/KyxS/e5hUYNWpU6tu3bxoyZEjzIqh5KQV22mmnNHr06LTzzjuXMn5BN7fAmDFj0sqVK1P37t2bG0LtSycwcuTI1Lt37zR06NDSxd6sAbc6ARqTynz+858vzGvBggVp5syZ6atf/WphZSqIQAj06dMn7bXXXjAIlE6gS5cuac899yxd3AImEAKDBg0CQaCUAvEhZu+99y5l7IJuboE4bxgxYkRzI6h9aQV84V/apmv6wHv16uW8oWS9oNUJ0Ph2MWYmdsVmyVpauAQIECBAgAABAgQIECBAgAABAgSaUKDVCdBdd901HXHEEemRRx5JPXv2TLvttltq621ukUSdO3duijFALQQIECBAgAABAgQIECBAgAABAgQIEChaoNUJ0AjgpJNOSqtWrUqPPfZYilsu2rNEEvQLX/hCe4rwXgIECBAgQIAAAQIECBAgQIAAAQIECLQo0KZZ4MePH5/OOuusdic/I6JIoH72s59tMThPEiBAgAABAgQIECBAgAABAgQIECBAoD0CbUqAHnzwwenII49sz37Xv/fVV19NMXPh6aefvv45PxAoQmDZsmXpueeeS2+++WYRxSmDQIcJxJXxzz//fFqyZEmH7dOOCBQlEEPbvPLKK0UVpxwCHSawfPnyNGPGjLRmzZoO26cdEShCIM4bXnjhhbR48eIiilMGgQ4VmDdvXpo1a1aH7tPOCBQh4LyhCMWOLaNNCdC4avPwww8vJNKHHnooffjDH0777rtvIeU1YiHPPPNMzWHFsAJ/+Zd/mY455pg0ePDgbFax973vfenyyy9PCxYsqLkcG6Y0bdq0NHny5DR79mwcBEolMH/+/DRp0qT09NNPlypuwRIIgccffzwbIseXT/pD2QSmT5+epkyZIoFftoYTb3rttdeySWqj/1oIlE3giSeeSBMmTEirV68uW+jibXKBuNgqjrszZ85sconyVL9NCdAiq7f99tunX/ziF+muu+4qstiGKuvSSy/dZjwrVqxIF110UTbB1A9+8IP0n//5n2nOnDnZVWC33npruuyyy9KoUaPSlVde6YrGbWr+eYO1a9dmP+TrGt9mMwKdLpD32Xzd6QEJgEArBPJ+m69b8VabEuhUgbzP5utODcbOCbRCIO+z+boVb7UpgU4XyPttvu70gARAoEaBvM/m6xrfZrNOFGjTJEjbijeuuLvxxhuzq0DiSqaVK1e2+JY33ngjxbftsdxyyy3pxBNPbHG7sj8Zyc34ZuCtb33rFqvy5S9/OV133XWbvd6jR4/Up0+ftGjRorRw4cJ0ySWXpJdeeil95zvf2WxbTxAgQIAAAQIECBAgQIAAAQIECBAgsLFA4QnQP/7xj+noo4/OknQb72rrvx177LFb36DEr+63337ptttu22ICNG4XjCs7Y9luu+2yK0FjjNUDDzwwGxqge/fuKcaz/P3vf59+9KMfpauvvjqNHj06ffSjHy2xSv1Dj8RxLPm6/nu0BwLFCPTu3TsrSN8txlMpHSsQ/Ta++OvWrVvH7tjeCLRTID/m5ut2FuftBDpMIM4bYogyfbfDyO2oQIHotzGObXwOthAok0B+zM3XZYq9WWMt/ChzyimnZMnPQYMGpZ133jk7kMVkHnvttddGxnGQi6s/999//3TBBRekeF9VlxNOOCF94hOfSBdeeGHq16/fZtW88847U1w2PXz48HTzzTent7/97ZttE/+p/uIv/iJ7vOMd78huiZcA3YxpoydiXNkRI0akXr16bfS8Xwg0ukAcJ8aPH5/iCnALgbIJxJeg8Te+a9dOH2WnbHTi7WSBGGpo2LBhzhs6uR3svvUCMaRY3EkXF01YCJRNYNy4cdl5gy9Oy9Zy4h05cmQaOnSo84YSdYVCP53ELe0x8cz//b//N8UssM8++2w2CU1M4vPLX/4y+zkmpYlH3BIeg8ZGkvTss8+u9JUikbB8/fXX02c+85kWu8Yf/vCH7PkbbrihxeTnpm+KxGckRE3us6nMxr/HN+GSnxub+K08Aj179syu5ihPxCIl8GeBuILDh3C9oYwCzhvK2GpizgXivMEXT7mGdZkEnDeUqbXEuqmAfMOmIo39e6EJ0EjknXzyyWnT29nPP//8dNVVV20msccee6S41TuujKzyEsnKiy++OF1//fXZ2Kib1jW+td1pp51SfPtV6/KWt7wl5YnTWt9jOwIECBAgQIAAAQIECBAgQIAAAQLNJlBoAjRu49511103MzzuuOPSQw89lBYsWLDZa3F7+L/927+lCRMmbPZalZ741Kc+lQYMGJDOOeecLBm6atWq9dU75JBDsrE+ax33JJxjlvh99tlnfRl+IECAAAECBAgQIECAAAECBAgQIEBgc4FCE6B77rlndvv7pruJW4rOPffcdOqpp6YNE3+xXdwyH8tNN92Urav6T9zqH1eAxvLd7343u9U9v4IzxvacOnVqzVW/8cYbs1u7Nx1XteYCbEiAAAECBAgQIECAAAECBAgQIECgSQQKTYDuvvvu2ViXkdD727/92+y296VLl2aUcZt7JPlisqOHH344S4Q+8MAD6Yorrshef/HFFytPHmOhfvazn83qGTO/H3TQQdkQADG7e9z+fuutt27VIGbV/Zu/+ZssmRwTR1kIECBAgAABAgQIECBAgAABAgQIENi6QKGzwMeVnt/85jfTBz/4wfVXgsYM8DH+Z+/evdM3vvGNFBP4/Pa3v80G6Y5bufMlZuxuhuUf/uEfMos88ftf//VfKR5hd99996W/+7u/S+985zvTwIEDsyTxzJkzs8mibrnllswtEsqRSP3yl7/cDFztqmNMEjV9+vQ0ZsyY1Ldv33aV5c0EOlJg5cqV6bHHHstmI44ZiS0EyiQwadKkFH147NixZQpbrATSnDlzsi/rY2iiGJ/dQqAsAnGH3aOPPprNRjxixIiyhC1OAplATI4ckykfeuihJgDVJ0olkE/8HRe29evXr1SxN2uwhSZAA/EDH/hA+t73vpfNeL569ep01FFHrbeN2+BjHNC4CnLD5OegQYPSJZdcsn67qv/wta99LcUEUOGwePHirLrr1q3LDvyf/OQnt1r9mDQqhguIhKll6wKRAF24cGHW5yRAt27l1cYSWLRoUdZvu3XrliVBGys60RDYusDLL7+c1qxZk0aPHm02+K1TebXBBPLzhvnz50uANljbCGfrAq+//np23hCfD0ZIgG4dy6sNJxDnDZHEj0fPnj0bLj4BEdiSwKuvvprlG+K8QQJ0S0qN9Xyht8DnVfvEJz6R5s2bl+Jgdtppp+VPZ+u4hft3v/tdim3e/va3p5gh/sknn2xx8qSN3lixX6LeMSTA2WefXdMHxKFDh2YzyMdkUjvssEPFNFSHAAECBAgQIECAAAECBAgQIECAQH0ECr8CNA8zMuBbyoKfeOKJKR7NvgwePDj9+Mc/Tt/5znfSr3/963T33XenuOV91qxZ2ZUHo0aNymZ632+//bJEcgwjYCFAgAABAgQIECBAgAABAgQIEOg8AXfldp59W/dceAL0hRdeSDEbvKV2gZgh/rzzzssetb/LltsSiEm5YjyZAQMGbGtTrxNoKIEdd9wxGwd4yJAhDRWXYAjUIjB8+PBsDNDu3bvXsrltCDSMQJw3LFmyJDv+NkxQAiFQg0D//v2zfht3jFkIlE0gzhviM1uPHj3KFrp4m1wgLmiLoctiSEdLOQQKTYC++eab2RigEyZMSNttV2jR5dAUZUMJxAEpHhYCZROI8Y/GjRtXtrDFSyATOOCAA0gQKKXALrvskuJhIVA2gUgcOW8oW6uJNxfYf//98x+tCZRKIBKfkp+larJU+Bigf/jDH1J8+Pnnf/7ntGzZsnJplCDaK6+8Mhvc/KKLLipBtEIkQIAAAQIECBAgQIAAAQIECBAg0LkCdblMM27DuPzyy9PnP//59JGPfCRdeOGF6cADD+zcmlZg73F59Re/+MVshrzrrrsunXXWWemoo46qe80ef/zx9Itf/CKtXbu23fu69957szJiAqgpU6ZkEzoNGzasxXJjIq14rFu3Lpv1frfddks77bTTZtvG688//3xasWJF9lrMnL333nu3OLnU8uXL04svvri+LjGhlP3z36xT/ekJ/c//P8cfx19/f/z9jfG9nH84/3L+2bnn3y2dpzn/9/nH5z+ff5v1839Lx0TP1SZQeAL0zDPPTD/5yU+ypNX999+ffvCDH6QjjjgijR49OkuEnn766clkPrU1zqZbxaRSMSZgjLMaY6tFkq8jlq9+9avpP/7jPwrd1V133ZXGjx+flRkfLFoaK27ixIlp6dKl6/f7+uuvpyOPPHL97/kPMV7X5MmT81+zdfSxGE9m0+Wll15K06ZN2+hp++ev/20+VqP/f44/jr/+/uR/LP39df7h/Mv5Z348yNcdef49cODAfLfr1x25f/1f/1/f8f7/D/qfz9+dmX/YtD/6vXaBLn/69mxd7Ztve8s5c+akXXfddaMNFy5cmCVF/+Vf/iW9/PLL2ZWLcVWoccI2Yqrplz/+8Y/pZz/7WTr++OPTQQcdVNN72rvRM888k2699dYsqd3esq6++ur0yiuvpL/4i79IcRVr3759U0w60NLy2muvpQULFqx/KfpVJIFbWqJfrVy5Mnupa9euWfKzpXFoY5swzLu9/fPX//z/a+mY4vjj+Ovvj7+/+bHB+YfzL+efnXv+HZ8v99lnnzRq1KiNLmRw/u/zj89/Pv/G3+pm+Px/9tlnp5tuuindeOONWT4tP0exbp1A4QnQbe3+0UcfTf/6r/+afvrTn6a3ve1t6YILLkgf+tCHXBW6LbiKvH7IIYekJ598Mp188snpV7/6VV1rFUnOuCzeFcd1ZVZ4nQSi78akBvEH3UKgTAJr1qzJhhkxm2uZWk2sIeC8QT8os0CVzxumT5/eYgK0zO0l9v8WcN7w3xZ+Kp9ADLHXEfkGCdBi+kaHf7KOq63iEWMvPvzww+ncc8/NbuuOyX0sBIoUePbZZ9Odd96ZjeVYZLnKIlBvgcWLF6c77rgjxW3oFgJlE3jggQfSPffcs36s5bLFL97mFYghcuK8Ia42sxAok0DcjhvnDU899VSZwhYrgUzgoYceSnfffXd68803iRAolcCMGTOy84bZs2eXKu5mDrbwMUBbwozJc26//fbslufbbrtto4PbzjvvnM4777x0yimntPRWzxFos0B8GxNLvm5zQd5IoIMF8j6brzt493ZHoF0C0W/jao74IOMK5nZRenMHC+TH3Hzdwbu3OwJtFsgnAtF320zojZ0osGzZsrR69ers3CEms7UQKItA9N1YHHvL0mIpFZoAjQ87f/u3f5v+1//6X5lAjPUYkyDFLe8xRsuGy5gxY9Jf/dVfpTPOOKNDLhnecN9+JkCAAAECBAgQIECAAAECBAgQIECgOQQKTYAGWUxsE2MgxKXsMXN4XAWSLz179szG+4zE5+GHH54/3bTr+MYgroiNS6dnzpyZZs2aleLW1wEDBqSY7XDEiBHpHe94R4pksW/DWt9N8iuP2LXezjs6VyDvu/m6c6OxdwKtE8j7bb5u3bttTaDzBPI+m687LxJ7JtA6gfxcV99tnZutG0Mg77f5ujGiEgWBbQvkfTZfb/sdtuhsgcIToHHV5+c///mN6jV8+PB00UUXpY997GNp0KBBG73WjL/ce++96Uc/+lE2CdDSpUu3SRAzT5566qnpC1/4QjYA+DbfYINMIGaLDLvBgwcTIVAqgfgC5MADD8y+CClV4IIl8CeBsWPHuo1NTyilQMww3bdv32xs+lJWQNBNK7DTTjul0aNHpxhazEKgbAJxsU/M5t69e/eyhS7eJhcYOXJkdvHf0KFDm1yiPNUvPAGaV71Lly7p+OOPz25zP+mkk1zB+CeYuXPnpo9//OPpl7/8Zc5U0zquCr3++uuzpOmFF16Yrr766rTddnVruppiKsNGffr0SXvttVcZQhUjgY0E4vi55557bvScXwiURcAXnWVpKXFuKhB3MO29996bPu13Ag0vEOcNI/5055iFQBkF4ot/C4EyCvTq1ct5Q8kari5ZtBNOOCH94z/+YzrggANKxlG/cOfPn5+OOeaYNHXq1Gwnw4YNS29729tSXG2wxx57pEjWxSP+E8UgujGbYzwi+RnvefTRR7Nb5L///e+n5557Lt16662pR48e9QtYyQQIECBAgAABAgRKJhBzEsybN2+zqGPoqTjX3nTp37+/+Qg2RfE7AQIECBCooEDhCdBDDz00G9cyH4umgmZtqlLc/h9jfX7yk59M559/fnZ7a2sLiuEFvvWtb6Xvfe976ZprrkmXXHJJa4uwPQECBAgQIECAAIHKCpx44onpnnvuqbl+cft4XFwQawsBAgQIECBQXYFCE6Ax+Ot3vvMdt7tv0l+eeeaZdMcdd6T77rsvHX300Zu8Wvuvu+++e3b7ewwpcMYZZ6RPf/rTyYC7tfvZkgABAgQIECBAoNoC++23X5oyZcpGlYy7q15//fXsTqsdd9xxo9fi1vG4A8tCgAABAgQIVFuga5HVi/FnZs+enU477bR0++23F1l0qcv6/e9/n2LszvYkPzcEiCEGYqyUF154YcOn/UyAAAECBAgQIECgqQXiLqn4PLLhI8bPjyU+o2z4fPz8yCOPuAW+qXuMyhMgQIBAswgUmgCN8SrPOuus9POf/zx96lOfahbDbdYzJj9661vfus3tWrPB4YcfnmbOnNmatzTdtjHWU9zSFGNBWQiUSWDdunXp+eefz8YBLlPcYiUQAvE3L4ZssRAom0BcJRjDFa1Zs6ZsoYu3yQXivCEujIjPYhYCZROIMXtnzZpVtrDFSyCbu8V5Q7k6QqEJ0LVr16a4CjSW97///eWSqGO0Q4cOTY8//nhhe4iTnKeeeiqNHj26sDKrWNC0adPS5MmTs2/6q1g/daquQEyaNmnSpPT0009Xt5JqVlmB+Hv32GOP+fKpsi1c3YpNnz49u3VaAr+6bVzVmr322mtp4sSJm936X9X6qle1BJ544ok0YcKEtHr16mpVTG0qLxAXW8WQKy5MK09TF5oAjTF1Tj/99GwcnUsvvbRmhTfeeCOb1KfmN5Rsw7ha84c//GH69a9/XUjkl19+eerevXvadAyjQgqvUCGRkI8lX1eoaqpScYG8z+brildX9SomkPfbfF2x6qlOhQXyPpuvK1xVVauYQN5n83XFqqc6FRfI+22+rnh1Va9CAnmfzdcVqlplq1JoAjSUvv3tb6eYCT6u/qh1mTp1aoornqq67L333ul973tf+sAHPpA+9KEPZTNTtvb2qriN+6677koxs2UkQC+44IKqcqkXAQIECBAgQIAAAQIECBAgQIAAgcIECp0FPpJ6Me7i9ddfnz7xiU+kf//3f88SdTvvvHOLAUem/MUXX0xf+tKX0nvf+94Wt6nKk9dee212O3aYxKN///7ZuKAjR45MMftk3759U58+fbJB2MMxrooNyxgPJWaRj9th4/aWWMLroosuqgpN3eoRnrHk67rtSMEEChbo3bt3VqK+WzCs4jpEIPrtihUrUrdu3Tpkf3ZCoCiB/Jibr4sqVzkE6i0Q5w0xDJm+W29p5ddDIPptDPG23XaFpibqEaoyCWwkkB9z8/VGL/qlIQUKPcq8/vrrKRJ68cEnX37wgx/kP251XfUEaMzaft9992Wzwd96660prB5++OHssVWYDV6Mk5tLLrkkff3rX9/gWT9uSWDffffNksu9evXa0iaeJ9CQAv369Uvjx49PPXr0aMj4BEVgawJHH3109kGma9fCbzLZ2m69RqDdAqNGjUrDhg3LhnJqd2EKINCBAttvv312l1gMkWUhUDaBcePGZecNvjgtW8uJN3JfMd+LfEN5+kKhn04GDBiQ3eJdnup3bKS77bZbuuWWW9Ltt9+ezjzzzLTDDjvUFMCgQYPSZZddll566SXJz5rE/rxRfBPuYNQKMJs2lEDPnj3XTyrXUIEJhsA2BOIKDh/Ct4Hk5YYUcN7QkM0iqBoF4rzBF081YtmsoQScNzRUcwimlQLyDa0E6+TNC70CNOpy7rnnph//+Mdpzz33zG5rj6To1i5nj7E//+3f/q2TGTp293FlVzziStm77747xayjs2fPTq+++mpavHhxikRpXIEQjz322CMbUzW/JbZjI7U3AgQIECBAgAABAgQIECBAgAABAuUWKDwBeuyxx6bhw4en2267Le2333416bzrXe9q1aRJNRVago3i24KTTjqpBJEKkQABAgQIECBAgAABAgQIECBAgEA5BQq9BT4I4taLT33qU2mfffapWeTII49MW5ooqeZCbEiAAAECBAgQIECAAAECBAgQIECAAIFNBApPgEb5n/nMZ7Y6Bs1jjz2W3eqdxxLJz4svvjj/1ZoAAQIECBAgQIAAAQIECBAgQIAAAQKFCBR+C3weVcxyfu2116aJEyemIUOGpL//+7/PX0rLly9PBx98cPr4xz+ePvvZz65/3g8EihSIcVVjfNUxY8akvn37Flm0sgjUVWDlypXZsCD5WMB13ZnCCRQsMGnSpBR9eOzYsQWX3LHFrVmzJt14440bfWEbEcTflhi//G1ve9tGAcUdMO9///uz8bs3esEvpRGYM2dOmjp1ajrkkENSzKptIVAWgVWrVqVHH300m414xIgRZQlbnAQygSlTpqQ33ngjm/ciJqOzECiLwNy5c9Ozzz6bDjrooNSvX7+yhN3UcdYlAXrPPfdkkyHNnDkzw40PBBsuxxxzTDYT+nvf+9705JNPpuuvvz7FzIUWAkUKxIfUhQsXpgULFkiAFgmrrLoLLFq0KOu33bp1k0ypu7YdFC3w8ssvp0gejh49utSzwd9+++3pvPPOaxVPJCAiaWopp0B+3hAJbgnQcrZhs0YdF57E+W4kj0ZIgDZrNyhtveO8IZL48ZATKG0zNmXgMYl15BvivEECtBxdoPAEaBzATj755LR06dKtCsQYoffff38aOXJk2nXXXdO3v/3trW7vRQIECBAgQIBARwkcd9xx6Stf+cpmV4BGgvO1115LZ511VhowYMD6cOIK0HPOOWf9734gQIAAAQIECBAgQKBxBApPgF5yySVZ8rNPnz7pqKOOSjvuuGN2K1xLVd59993TRz7ykXTVVVelM844Ix122GEtbeY5AgQIECBAgECHCsTQKV/72tc22+ddd92VJUAvvfTStP/++2/2uicIECBAgAABAgSqL2DIhvK1ceEJ0LvvvjsbOynWO+20U/rNb36TfvjDH25R5oADDkhr165NN998swToFpW80BaBSLDHeDIbXqHTlnK8h0BHC8QXRwMHDszGT+7ofdtfsQIxNtDq1as3KnTFihUphjfo3r37Rs/36tWrEser4cOHZ198blq/jSrrFwINKBDnDUuWLMmOvw0YnpAIbFGgf//+Wb8dOnToFrfxAoFGFYjzhvjM1qNHj0YNUVwEWhQYPHhwiqHLBg0a1OLrnmw8gUIToK+88kqKMWiuueaaLPlZS3VjzIRYYrIkC4EiBeKAFA8LgbIJxPhH48aNK1vY4t1E4Lvf/W66+OKLN3l267/+4he/SB/84Ae3vlGDvxpfbFoIlFFgl112SfGwECibQCSOnDeUrdXEmwu4myKXsC6bQCQ+JT/L1WqFJkDjpLF3797pwAMPrFnh4YcfzraNzLmFAAECBAhURSCuaIjHhleAxh0PMWB63DKz2267bVTV+Pu56XMbbeAXAgQIECBAgAABAgQIEGiTQKEJ0O222y4dfPDBafLkyenwww/fZkC//vWvU4ylFcvYsWO3ub0NCBAgQIBAWQTe9773pXhsuMTttTFLZMwwPWvWrA1f8jMBAgQIECBAgAABAgQI1Emga9Hlnnrqqemyyy5LMcbZ1pYYG/Tcc89dv8kRRxyx/mc/ECBAgAABAgQIECBAgAABAgQIECBAoAiBQq8AjYA+/elPp//8z/9MxxxzTLrooouy2/zi+bjqZerUqemZZ55J1157bcpvfY/Xjj/++HTOOefEjxYCBAgQaBKB3/3ud+mqq67KJsLLqxy3iD/77LMpJnQYMmRI/nS2jisnY1xNY/tuxOIXAgQIECBAgAABAgQIENiGQOEJ0BjX7IYbbkinnXZa+tjHPpbtPm6Njw+uLS2HHHJI+vnPf55iGwuBIgXWrVuXXYkc4+pZCJRNIK6ij0kNunYt/EL9hqH45S9/mSIJ2tIyc+bMNGXKlM1e+su//EsJ0M1UGuuJNWvWZElts7k2VruIZtsCzhu2bWSLxhVohvOGxtUXWXsEnDe0R897O1tg+fLl2Tw4nR2H/dcmUJes4w477JB9qI0Ptl/84hfTH/7wh82i2XfffdNXvvKV9OEPf7jSH/A3q7gnOkwgriKbNm1aevvb3252tg5Tt6MiBBYvXpzuu+++bAKdgw46qIgiG7KMK6+8Mp1++unpzTffXB/fyy+/nM4///y0xx57pH/9139d/3z8EF+kGS5lI5KG/OWBBx5IK1euTCeeeKK/7w3ZQoLakkCcM8S5Qxxndt111y1t5nkCDScQd9rde++92d/OuLjEQqBMAg899FB64403svOGbt26lSl0sTa5wIwZM7ILNg477DATmZakL9QlAZrXffz48Ske8Uc5TihffPHFrGPss88+KWaMtxCop0B8GxNLvq7nvpRNoEiBvM/m6yLLbqSy+vbtm975znduFFIMkxJLTBJ0wgknbPSaX8ohEP02ruaIxHaVr2AuR2uIsjUC+TE3X7fmvbYl0JkC+dwL+m5ntoJ9t1Vg2bJlafXq1dm5gwRoWxW9rzMEou/G4tjbGfpt22fh91ZGkjOu/NhwiStCDz300Oy2+KOOOmqj5OfFF1+c4monCwECBAgQIECAAAECBAgQIECAAAECBIoWKDwBeuSRR6Ynn3yy5jjPPvvs9IEPfCCtWrWq5vfYkEAtAvmVR75JrEXLNo0kkPfdfN1IsYmFwLYE8n6br7e1vdcJNIpA3mfzdaPEJQ4C2xLIz3X13W1Jeb0RBfJ+m68bMUYxEWhJIO+z+bqlbTzXWAJ1vQW+lqqOGTMmxbgfMTN8XA1qIVCUQAy1EGMGmjG6KFHldJTAwIED04EHHphibSFQNoGxY8e6ja1sjSbeTGDUqFEphuYYMmQIEQKlEthpp53S6NGj084771yquAVLIAQiHxB3kHbv3h0IgVIJjBw5MpsAaejQoaWKu5mDLfwK0NZgLly4MH3jG9/IDnj33HPB52BjAABAAElEQVRPa95qWwLbFOjTp0/aa6+9Uv6t+DbfYAMCDSLQpUuXtOeee6YYPsRCoGwCgwYNMhB82RpNvJlA796909577+28QX8onUCcN4wYMSL74r90wQu46QXiC39fPDV9NyglQK9evbLzhu226/TrCkvp1xlBt7ulYgbfX/ziF+tjj0kPjjvuuJR3gviD3NIjBuuO2d7ypX///vmP1gQIECBAgAABAgQIECBAgAABAgQIEChEoN0J0J/97GfpqquuSp/73Oey2dsiqtbOghW3G5133nmFVEghBAgQIECAAAECBAgQIECAAAECBAgQyAXanQCNgj71qU+lGPPrpJNOSosWLUoHH3zwNm/BiIFi99hjjxTjLZ1yyinpLW95Sx6TNQECBAgQIECAAAECBAgQIECAAAECBAoRKCQBGpGMGzcu/fa3v00nnHBC+qd/+qd0xBFHFBKgQggQIECAAAECBAgQIECAAAECBAgQINBWgUInQYok6G9+8xuTH7S1NbyvUIFly5al5557LsW4tBYCZRJYt25dev7559OSJUvKFLZYCWQCc+fOTa+88goNAqUTiCGcZsyYkdasWVO62AXc3AJx3vDCCy+kxYsXNzeE2pdSYN68eWnWrFmljF3QzS3gvKF87V9oAjSqf/zxx6fhw4e3SmL16tXp7rvvbtV7bExgWwLTpk1LkydPTrNnz97Wpl4n0FAC8+fPT5MmTUpPP/10Q8UlGAK1CDz++OPpscce8+VTLVi2aSiB6dOnpylTpkjgN1SrCKYWgddeey1NnDgx67+1bG8bAo0k8MQTT6QJEyasn0+kkWITC4GtCcTFVnHeMHPmzK1t5rUGEig8AdqWut15553pwQcfbMtbvYfAFgXWrl2bvZavt7ihFwg0mEDeZ/N1g4UnHAJbFcj7bb7e6sZeJNBAAnmfzdcNFJpQCGxVIO+z+XqrG3uRQIMJ5P02XzdYeMIhsEWBvM/m6y1u6IWGEShsDNANaxQd4JFHHknPPPNMmjp16lZnhY9tIvl56aWXbliEnwkQIECAAAECBAgQIECAAAECBAgQINBugcIToDGGx5lnnumW9nY3jQLaK9CnT5+siHzd3vK8n0BHCfTu3Tvblb7bUeL2U6RA9NsVK1akbt26FVmssgjUXSA/5ubruu/QDggUJBDnDV26dEn6bkGgiulQgei3MY7tdtsVnpro0HrYWfMJ5MfcfN18AuWrceFHmbPOOkvys3z9oJIR77vvvmnEiBGpV69elayfSlVXoF+/fmn8+PGpR48e1a2kmlVW4Oijj84+yHTt2hCj7FTWWcWKFxg1alQaNmyY84biaZVYZ4Htt98+nXjiial79+513pPiCRQvEBMpRwLUF6fF2yqxvgIjR45MQ4cOdd5QX+ZCSy80Abpo0aJ07733ZgGefvrp6eSTT06HH3542mGHHVoMOmbZjJliv/zlL7f4uicJtEcgvgmX/GyPoPd2pkDPnj07c/f2TaDNAq7gaDOdN3aygPOGTm4Au2+XgPOGdvF5cycKOG/oRHy7breAfEO7CTu0gEIToKtWrUox/udhhx2WfvrTn9ZUkcGDB6dvfvOb6Ve/+lVN29uIAAECBAgQIECAAAECBAgQIECAAAECtQoUmgDdZZddsuTnnnvuWev+s+1Gjx6dWvueVu3AxgQIECBAgAABAgQIECBAgAABAgQINKVA4QN0xa3vd911V4rb21uzzJgxozWb25YAAQIECBAgQIAAAQIECBAgQIAAAQLbFCg8AfrXf/3X6YADDkjXX3/9NneebzB58uR0yy235L9aEyBAgAABAgQIECBAgAABAgQIECBAoBCBQm+BjzFAp0+fnq644op0xhlnZFeBxm3xW1teeumldOONN2YTJm1tO68RaK3A7Nmzs/44ZsyY1Ldv39a+3fYEOk1g5cqV6bHHHstmI44ZiS0EyiQwadKkFH147NixZQpbrATSnDlz0tSpU9MhhxySYlZtC4GyCMRnsEcffTSbjXjEiBFlCVucBDKBKVOmpDfeeCMdeuihKSajsxAoi8DcuXPTs88+mw466KDUr1+/soTd1HEWmgBdvnx5dtIYf4Rj+au/+quacWPGeAuBIgUiAbpw4cK0YMECCdAiYZVVd4FFixZl/bZbt25ZErTuO7QDAgUKvPzyy9kXoDG+d/fu3QssWVEE6iuQnzfMnz9fArS+1EovWOD111/PzhsieTRCArRgXcXVWyDOGyJ/EI+ePXvWe3fKJ1CYwKuvvprlG+K8QQK0MNa6FlToLfD9+/dP73rXu+oasMIJECBAgAABAgQIECBAgAABAgQIECBQq0ChV4DGTk855ZT0H//xH+nUU0/NZoTv3bv3FmNZt25dimz5TTfdtMVtvECAAAECBAgQIECAAAECBAgQIECgUQQM2dAoLVF7HIUnQN///venvfbaK91888013/p29NFHpwcffLD2qG1JoAaB3XffPRtPZsCAATVsbRMCjSOw4447poEDB6YhQ4Y0TlAiIVCjwPDhw7MxQN3+XiOYzRpGIM4blixZkh1/GyYogRCoQSDuwovzhqFDh9awtU0INJZAnDfEGKA9evRorMBEQ2AbAoMHD04xdNmgQYO2saWXG0Wg8ATozjvvnK655pqak58Bcdxxx6X4wG8hUKRAHJDiYSFQNoEY/2jcuHFlC1u8BDKBAw44gASBUgrExJ3bmryzlBXbJOiPf/zj6ZFHHtno2RjHf9asWWm33XbbbNz0mNzhRz/60Ubb+6WxBCJx5LyhsdpENLUL7L///rVvbEsCDSQQiU/JzwZqkBpCKTwBGvt897vfXcOu/3uTrl27mi32vzn8RIAAAQIECBAgQKAuAr/97W/TzJkzWyx7xowZmz0fidE333wzxcR8FgIECBAgQIBAWQXaNAnSOeeck2L8zqKWONn62te+VlRxyiFAgAABAgQIECBAoAWBSZMmpaeeemqjxyc/+clsy/POO2+j52O7Z599VvKzBUdPESBAgAABAuUSaNMVoHGbzEsvvZRGjBhRSG2nTp1aaEK1kKAUQoAAAQIECBAgQKBiAjHs1KZDT8Wt77HsuuuuafTo0RWrseoQIECAAAECBFJq0xWgAXfFFVekZcuWtdtw4cKF6eqrr253OQogQIAAAQIECBAgQIAAAQIECBAgQIDApgJtugI0Crn++uuzAdFj0NfevXtvWm5Nvy9dujQtWLAgu/rz7W9/e03vsRGBWgVimIYVK1a0uX/Wuh/bEaiHQPTdmNQgxki2ECiTwJo1a9LatWvN5lqmRhNrJuC8QUcos4DzhjK3XnPH7ryhudu/7LWPu6Pbmg8re93LGH+bE6BR2fiAM2fOnDLWW8xNIBBjVk2bNi1Fct3sbE3Q4BWq4uLFi9N9992Xhg8fnmL2XQuBMgk88MADaeXKlenEE0+UwC9Tw4k1O2eIc4cjjjgiuxUcCYGyCCxZsiTde++9aY899kiHHHJIWcIWJ4FM4KGHHkpvvPFGdt5gsjWdokwCMZfNlClT0mGHHZbyoWTKFH8zxurSomZs9Sapc3wbE0u+bpJqq2YFBPI+m68rUCVVaCKB6LerVq3KZo1uomqragUE8mNuvq5AlVShSQTi6s9Y9N0mafCKVTOG1Vu9enWKK0EtBMokkA8J6dhbnlZr8xWgcUXdxz72sewKpbZe8hu3wE+fPj394Ac/KI+YSAkQIECAAAECBAgQIECAAAECBAgQKI1AmxOgN9xwQ3r3u99dSEWHDRuW4tYNC4EiBfKxE91KUaSqsjpCIO+7+boj9mkfBIoSyPttvi6qXOUQqLdA3mfzdb33p3wCRQnk57r6blGiyulIgbzf5uuO3Ld9EWiPQN5n83V7yvLejhFocwL0mGOOKSzCo48+Ot12222FlVeVgmIslIULF6Y+ffqknXfeuSrV6rB67LPPPqlfv35p8ODBHbZPOyJQhMDAgQPTgQcemGJtIVA2gbFjx2a3seUfyMsWv3ibV2DUqFGpb9++aciQIc2LoOalFNhpp53S6NGjfV4oZesJesyYMdnY4d27d4dBoFQCI0eOzCZAGjp0aKnibuZg25QAPeecc7ITxKLg9ttvv2y27qLKq0o5N910U7rooovSKaeckn7+859XpVodVo9IHO+1114dtj87IlCUQJcuXdKee+5ZVHHKIdChAiad61BuOytQIIZ02nvvvQssUVEEOkYgzhtGjBjRMTuzFwIFC/jCv2BQxXWYQK9evZw3dJh2MTtq0yRI/+N//I9i9v7/S9lhhx1SXAVqIUCAAAECBAgQIECAAAECBAgQIECAQJECbUqAFhmAsggQIECAAAECBAgQIECAAAECBAgQIFAvgTbdAl+vYKpa7r/8y7+kf/zHf2x19RYtWpS9584770wxTMCmy7PPPrvpU34nQIAAAQIECBAgQIAAAQIECBAgQGADAQnQDTDq9WMM6Dx16tQ2F79kyZJ2vb/NO/ZGAgQIECBAgAABAgQIECBAgAABAiUXkADtgAY899xz0/bbb58uuOCCtHTp0vSe97wnbbfdtumff/759OSTT6Zhw4YZI7UN7bRs2bI0e/bsbFB4sxG3AdBbOk1g3bp16YUXXkgxmUyMkWwhUCaBuXPnZrPA77777mUKW6wE0vLly9OsWbOy84ZaztOQEWgUgThvePHFF9OAAQNSv379GiUscRCoSWDevHlp1apVaciQITVtbyMCjSLgvKFRWqL2OLadhau9LFtuReDUU09NRxxxRDrnnHPSq6++mn7yk59sc8aw6667LpsF/tBDD00xI7yldQLTpk1LL730UurZs2caOnRo695sawKdKDB//vw0adKkNHjw4HT44Yd3YiR2TaD1Ao8//nj2Qeakk05KvnxqvZ93dJ7A9OnTsy+fevTokX353HmR2DOB1gm89tpraeLEiWmXXXZJRx55ZOvebGsCnSzwxBNPpBUrVmT9N+6ctBAoi8Bzzz2X4hFfmo4YMaIsYTd1nCZB6sDmjyTc3XffnT74wQ+mww47LP3whz/swL03367Wrl2bVTpfN5+AGpdVIO+z+bqs9RB3cwrk/TZfN6eCWpdRIO+z+bqMdRBzcwrkfTZfN6eCWpdVIO+3+bqs9RB38wnkfTZfN59A+WosAdrBbda1a9f0+c9/Pt11113p7/7u71JcGRrf2loIECBAgAABAgQIECBAgAABAgQIECheoFMSoI899lhavHhx8bUpUYmHHHJIisv9Bw4cmA488MDsytAShV+KUPv06ZPFma9LEbQgCfxJoHfv3pmDvqs7lFEg+m3cQuz29zK2XnPHnB9z83Vza6h9mQTivKFLly5J3y1Tq4k1F4h+G7e+G3s5F7Eui0B+zM3XZYm7meOs2xigr7/+err22muz8WhiQOO///u/X+8cg8UefPDB6eMf/3j67Gc/u/75Zvsh/qOE0W9+85v0kY98JJ111lnpG9/4RjZmZbNZ1KO+++67bzYWR69evepRvDIJ1E0gJjAYP358lkSq204UTKBOAkcffXSKCTnijgcLgTIJjBo1Khv703lDmVpNrCEQk62eeOKJWRKJCIGyCYwbNy47b/DFadlaTrwjR47M5hpx3lCevlCXTyf33HNPOuCAA9IXv/jFdPPNN6eYjGbD5Zhjjkm33357ikl+IvG3cuXKDV9uup/f//73p6eeeipNnjw5Gxt0ypQpTWdQjwrHN+EORvWQVWZHCMTkXdGHLQTKJhBXcJjEoGytJt4QcN6gH5RZIM4bfPFU5hZs3tidNzRv21eh5vIN5WrFwq8Affnll9PJJ5+cli5dulWJffbZJ91///0psua77rpr+va3v73V7av+4m677ZZ+97vfpauuuiodddRR6fLLL3f1V9UbXf0IECBAgAABAgQIECBAgAABAgTqLlB4AvSSSy7Jkp9xe3ck8nbcccctXuG5++67Z1eARtLvjDPOyK5+rHuNG3gHceXBpz/96XTcccelM888M82dO7eBoxUaAQIECBAgQIAAAQIECBAgQIAAgcYXKPwW+LvvvjvFBD8zZ85Md9xxR5bI2xpD3Cq/du3a7Fb5rW3XTK/FpEgTJkxIF154YZYgjiEDLAQIECBAgAABAgQIECBAgAABAgQItF6g0CtAX3nllRSTH11zzTVpp512qimahQsXZttNnDixpu2bZaMYS+KKK65oluqqJwECBAgQIECAAAECBAgQIECAAIG6CBR6Beguu+ySevfuneIKxlqXhx9+ONt00aJFtb6lqbe78sors5nNL7rooqZ2qKXys2fPTg888EB64403atncNgQaRiAmhnvwwQdTjKlsIVA2gUmTJmV3MZQtbvESmDNnTnbesK1x7EkRaDSBVatWZecNL774YqOFJh4C2xSICYAfffTRbCb4bW5sAwINJBBDFka+YfHixQ0UlVC2JlDoFaAxg9vBBx+czWZ++OGHb22/2Wu//vWv01133ZX9PHbs2G1u3+wbRJL4i1/8YoqTnOuuuy6dddZZ2Tir9Xb58Y9/nK699tpC/ihNnTo1CzcSO3Gw2H777bM+09Js19OmTUuvvvrq+uoNHz48xWPTZc2aNenxxx9fP9Zst27dsiR8JEDjCuMFCxakvn37Zm977bXXUvyRXbduXfZ7Pfe/ww47bBpqsn/+tfS/+L8e/TauqM8/zLS2/5e9/0USOI4R+VKV+j/22GNZleK49cQTT9Tt+NeZ7R/H3TfffDONHj16o9ngHf8c/2o5/sV/kPb+/W9r/8/PGyLOOAblS1WOP43un3vXo/3zpHae5K7a+d/q1auzORhiWLERI0bklOvXZT/+rq/IBj9s6fy/rf//86Lr0f/K7t+az19t8Y/PhfH5Nh4vvfRSuz7/tWX/edvHWvu37/N3s/nHOW+cL0QCP/JfHVX/Dfusn1snUGgCNHZ96qmnpssuuyz96le/SnEb95aW3/zmN+ncc89d//IRRxyx/mc/tCzQr1+/NGTIkPTCCy9kHyr33nvvljcs+Nnbb789PfLII4WWGt+SxAEj1nHFcCTPN13ig8iGVwbH1cUt/QGOg86GidIoZ0vfwsQJSDzypZ7739IB0P7519L/Yps4uY//J7G0tv+Xvf9tWPcq1T9vz/gSJoaNqdfxrzPbv2vXlm8ucfz196csf//iy6fly5dnx94qHX/K4t/e87+Wjn/53UCRYKn3+WdL+++o41/87Wxp6aj9x3l1Peofk+puumzp/L8e+2/N5w/7b/kCkGY+/pT9/5/+v/kFWJsef+JvTL2Ofy35b3o89HvtAl3+9CHsz5fC1f6erW4ZxZ1yyinZJEhxm3Zc2RfJzhtvvDHF1X/PPPNMdjVhfut7FHb88cenSLK1lATb6s6a8MU//vGP6Wc/+1lmdtBBB3WIQHwIiFsa41vl9i7/83/+z+ybtfe85z1Zn4ikTjxaWuLb7CVLlmQvRT+KE4ot9ZE46MSBKJa4ArR///7Z1VXhFVclDxs2LHst+md8sMnrUs/9Zzvc5B/7519L/4srVP7rv/4rG0s5JoprS//fpOuVpv/H34j9998/7bfffumhhx7K4q5S/WOCwDgexdVHcdtMvY5/ndn+MXxDfAiP43z37t3Xh1KV499b3/rW9PTTT2d3E0Rfbenvz/pKb/BDVeq/QZWyH6tU/6eeeiq7+iiOu/lY9lU6/mzt7883vvGN9KUvfSldeuml6fLLL2/X+demfSR+7+z+f8MNN2QXXnzoQx9K3//+97Njb5WOv3HBQJyrDxw4MI0bN26zJuhs//buPy7+2GeffdKoUaOyzxF5Bat0/Mnr1N7PP3k5G67b69+az18b7jf/eVv7jzxAfDkxfvz4FF+itufzX77PDdfb2v+G2zajv/r/t0Br2z/OB+OChpEjR6Y4P2xpKar/nX322emmm27KcihxJ7ClbQKFJ0AjjDhonXbaadks8PF7HDS39I1kzBh/7733Zgmr2NZSbYFo7yeffDKdfPLJ2VXC9axtXBU6ffr0FPvMb4Gv5/6UTaAogUjmT5gwIe2xxx7rk/dFld3o5eQJ0Le85S1ZkqnR421tfPH3Ma7mjy90tnSlemvLbLTtJ0+enH0hNWbMmEYLrZB4Nk2AFlKoQhpCIL6UiC/r44vT+JKimZYNE6Dxc9WWGM4pvoSPD5Dxc9WWSB7FECtDhw5t8W6pstc3zudbSoCWvV7i/7NAJJEimR1D4sWXThYCZRGYN29eevbZZ1NcmNbS1d9F1kMCtBjNze87LqDcaPzf/e532SPGrPzDH/6wWan77rtv+spXvpI+/OEPZ9/0bLaBJwi0U2Dw4MEpHhYCZRPo2bNni1dwlK0e4m1Ogbh6zkKgjAIxmWc8LATKJtCjRw/nDWVrNPGuF4i7KSwEyigwaNCgFA9LeQTqkgDNqx+XsccjrniJzHhM5rHbbrtl3+A5wcyVrAkQIECAAAECBAgQIECAAAECBAgQqJdAXROgedBxReihhx6aPeK5FStW5C9ZEyBAgAABAgQIECBAgAABAgQIECBAoG4CdUmAzpgxI1155ZXZ+EkxjlBMSrPhEmPbXXfddemb3/xmNlbNhq8108/Lli1Lt912WwqvmBhj1qxZ2ZhwAwYMyAYxHzFiRHrHO96RYhy1TQ2byUldCRAgQIAAAQIECBAgQIAAAQIECLRVoPAE6EsvvZSOPfbYbDasCOpd73pXNmP5hgEeddRR2SQQcXv8xRdfnC644IINX678zzHp049+9KNsEqClS5dus74xYcapp56avvCFL2TDB2zzDTYgQIAAAQIECBAgQIAAAQIECBAgQCAT6Fq0w1e/+tX1yc+BAwemmMm3peXAAw9M1157bbrooovqPht4S/vvjOdidtFTTjklvfOd70w33nhjqiX5GXHGTMHXX399ZvmJT3wirVmzpjPCL90+161bl5YvX166uAVMIARiqJC1a9fCIFA6gfgbFTMSWwiUTcB5Q9laTLwbCjhv2FDDz2UScN5QptYS66YC8g2bijT274VeAbpy5cr0k5/8JI0cOTL9zd/8TTbDe//+/bcoEFeCjhs3Lp177rnp4IMPTnHLd1WX+fPnp2OOOSZNnTo1q+KwYcPS2972tjRq1Ki0xx57pD59+mSPXr16ZUm7mDgqHpH8jPc8+uij2S3y3//+99Nzzz2Xbr311hQzPlq2LBATb02bNi29/e1vNzvblpm80oAC8f/+vvvuS8OHD08HHXRQA0YoJAJbFnjggQdSnA+ceOKJqWvXwr9n3fKOvUKgnQJxzhDnDkcccUTadddd21matxPoOIH4zBB3mMVnikMOOaTjdmxPrRb46U9/mj760Y+m1atXb/TeN998M/t902HPevbsmX75y19mf1M3ekOFfnnooYfSG2+8kdVx0/pXqJqqUkGBGMpwypQp6bDDDssm+65gFStXpUIToC+88ELacccd0+TJk1McrGtZIgn44IMPZleBfuYzn6nlLaXc5mMf+1g21ucnP/nJdP7556e4Ara1yyuvvJK+9a1vpe9973vpmmuuSZdccklri2iq7fNvY/J1U1VeZUstkPfZfF3qygi+6QSi38bVHPFhTgK06Zq/1BXOj7n5utSVEXxTCeQTzOq7jd/s8fcxkp95wnPTiDd9fmvbbvresv4e82JEPcNGArSsrdiccUffjcWxtzztX+ilGdttt112tVKtyc9giol/YnnkkUeydRX/eeaZZ9Idd9yRXdH13e9+t03Jz3DZfffd09VXX51uv/329PWvf93tsVXsLOpEgAABAgQIECBAgEAlBc4666xsmJhI9uWPuII3lrgTMH8uX8cdFe95z3sqaaFSBAgQ6GiBQhOge++9d5o9e3bNdYjx7R5++OFs+7jsvarL73//+3ThhRemo48+upAqnnDCCdks8XHFrWXLAvmVR75J3LKRVxpTIO+7+boxoxQVgZYF8n6br1veyrMEGk8g77P5uvEiFBGBlgXyc119t2WfRns22inabMNHxNilS5eNnovX47mqL3m/zddVr6/6VUcg77P5ujo1q25NCr0FPg7Qu+22W/o//+f/pA996EPbVPvrv/7rFGNjxhLjJlR1icmP3vrWtxZavcMPPzzNnDkzRdLZ0rLAPvvsk/r165cGDx7c8gaeJdCgAjGBXAyTEWsLgbIJjB071m1sZWs08WYCMS57375905AhQ4gQKJXATjvtlEaPHp123nnnUsUtWAIhMGbMmGzs8O7duwMhUCqBmPumd+/eaejQoaWKu5mDLTQBGpBXXHFFNoBx3A7/wQ9+sEXb1157LV122WXpn/7pn7LX49ut9773vS1uW4Un4z/E/fffX1hVYpbSp556KjvRKazQChYUE0vttddeFayZKlVdIL5M2nPPPateTfWrqMCgQYMqWjPVqrpAfIjxxXLVW7ma9YvzhhEVnky2mq2mVrmAL/xzCeuyCcSwFc4bytVqhSdAY+bMr371q+mUU05JcRXIaaedln2QHzBgQHr++efTxIkT0w033JDNbp5TXXrppdm2+e9VW8fVmuf+aab7d7/73enkk09ud/Uuv/zyFN+QxYRTFgIECBAgQIAAAQIECBAgQIAAAQIEtixQeAI0dhWzk8cMdpGo+8IXvrDlvf/plY9+9KNZwnSrG5X8xfhW4H3ve1/6wAc+kCWEYzzQY489NsVVsrUu4Xnvvfem//2//3e688470/e///1a32o7AgQIECBAgAABAgQIECBAgAABAk0rUHsGrpVEn/vc59KHP/zhFFd33nPPPRtNjhS3GMU4Nd/61reyRGAriy7l5tdee22aPHly+vd///fs0b9//2xc0Bg3Im5ZiTGn4pbtsIlZ/2JSqGXLlqVZs2almEV+0qRJKYYOiOVLX/pSuuiii0rpIGgCBAgQIECAAAECBAgQIECAAAECHSlQtwRoVCLGvrzxxhuz+ixcuDBNnTo17brrrmn48OGp2WbKirFN7rvvvmw2+FtvvTW9/vrr6eGHH84etTZ4JEfj6tqvf/3rtb7FdgQIECBAgAABAgQIECBAgAABAgSaWqBrR9U+ZieM8UFjYo9Nk5+rV69Od999d0eF0mn72W233dItt9ySbr/99nTmmWemHXbYoaZYYkKJmDTqpZdekvysSezPG8UVtM8991w2HEMr3mZTAp0uEBOdxZjJS5Ys6fRYBECgtQJz585Nr7zySmvfZnsCnS6wfPnyNGPGjOxOnE4PRgAEWiEQ5w0vvPDCRnMstOLtNiXQqQLz5s3L7nrs1CDsnEAbBJw3tAGtk99S1ytAa61bjGn52GOPpeOPP77Wt5R6u/Hjx6d4rFixIkv8Tp8+PRsi4NVXX81OXCJROmzYsOyxxx57pEMPPTS7Nb7Ule6E4KdNm5YljXv27JldjdwJIdglgTYJzJ8/Pxv2YvDgwSkmUbMQKJPA448/nlatWpVOOumk1K1btzKFLtYmF4jzsUgi9ejRIzsHa3IO1S+RQAyTFRPN7rLLLunII48sUeRCJZDSE088kX0ujv4bE/1aCJRFIC62ikfM7RLDGloaX6AuCdC1/4+98wCTotje/iEvackI7JJZkJzBS5as14QgiqKiXhUBFVABBRUDYgAVRAFRFEEURUG4imSJkgQksyRBYMmZJcP3f+t+te4uM7sTema6ut96nqF3uqurzvl10VN9+tQ5V6/K77//rmJXYtk7LOPeCuJbLl68WMUK9VbHqfujoqLUA6JT9Yu0XhiHKHobaXnYPwn4SkCPWb319TzWIwE7ENDjFlsaQO1wRSiDrwSSj11fz2E9ErADAY5dO1wFyhAoAY7fQMnxvEgT4NiN9BXwv3/LDaBwYcfybjcsafcfN88gARIgARIgARIgARIgARIgARIgARIgARIgARIIJwHLDaCdO3em8TOcV5B9eSWQI0cOdUxvvVbkARKwGQEkPEPh2LXZhaE4PhHAuEWIF3p/+oSLlWxEQN9z9dZGolEUEkiTAOYNGTJk4LwhTUo8aFcCuOciji2WEbOQgEkE9HxBb02S3a2yWnqXOXHihMybN0+xvPfee+Wuu+5S8eu8Jfu5fPmySpQwYMAAt/Kn3iEkUKFCBRWLA6EGWEjAJALR0dEqTjDi0LGQgGkEGjdurB5kUic8NE0Pyus+AnFxcSr2J+cN7rv2pmucK1cuadOmDeMnmn4hXSp/w4YN1byBL05dOgAMVrtcuXIq1wjnDeZcREsNoEh6gDgI9erVk2+//dYnCkjyMXjwYJkyZYpP9VmJBHwlgDfhvBn5Sov17EYAybtYSMBEAvTgMPGqUWYQ4LyB48BkApw3mHz13C075w3uvv6ma097g1lX0FIDKDK3wfhZunRpvyhUr17d73P86oCVSYAESIAESIAESIAESIAESIAESIAESIAESIAEXEkgo9VaY+n77NmzBcvb/Snbt2/3pzrrkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEC6BCw3gD799NNSpUoVGTt2bLqd6wobNmyQadOm6a/ckgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAlBCxdAo8YoNu2bZM33nhD7rvvPuUFimXxaZXdu3fL+PHjVcKktOrxGAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn4S8BSA+i5c+ekVq1aAkMoSvfu3X2WBxnjWUjASgIJCQnKIF+7dm3JmTOnlU2zLRIIKYELFy7IypUrVTbiEiVKhLQvNk4CVhNYv369YAzXqVPH6qbZHgmElMDBgwdl69atai6LrNosJGAKATx7rVixQmUjLlWqlCliU04SUAQ2btwoZ8+elbp166pkdMRCAqYQOHTokGzZskVq1Kgh0dHRpojtajktXQKfJ08eadWqlauBUnn7EIAB9Pjx43L06FH7CEVJSMAHAidOnFDjdt++fT7UZhUSsBeBPXv2CMbupUuX7CUYpSGBdAjoecORI0fSqcnDJGAvAidPnuS8wV6XhNL4QQDzBtx/tROVH6eyKglElMCBAweUvYHzhoheBr86t9QDFD23b99efv75Z+nQoYPKCJ89e3avAl27dk0wWCZMmOC1Dg+QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQKAELDeA3nnnnVKmTBmZOHGiZMmSxSe5GjduLIsXL/apLiuRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQKQIZMiQIVJds98ACVhuAM2fP7+MGDHCZ+Mn5G7evLnkzZs3QBV4Ggl4JlCsWDEVT6ZAgQKeK3AvCdiUAO6HBQsWlJiYGJtKSLFIwDuBkiVLqhigvr4E9d4Sj5BAeAlg3nD69Gl1/w1vz+yNBIIjgDBkmDfExsYG1xDPJoEIEMC8ATFAs2bNGoHe2SUJBE6gSJEigtBlhQoVCrwRnhlWApYbQCH9LbfckqYSSO5RoUKFpECxGTNmZLKENInxYCAEcEPCh4UETCOQLVs2adiwoWliU14SUASqVKlCEiRgJIHChQsLPiwkYBoBGI44bzDtqlFeTaBSpUr6T25JwCgCMHzS+GnUJRNLkyAlVx3BuN955x154IEHpE+fPskPCbLF16xZU4YMGZJiP7+QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgJUEQuIBOnfuXOnSpYvs3btXyYq4oMlLkyZNZMaMGfLvf/9b1qxZI2PHjhV4PLGQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgJUELDeA7tmzR+666y45c+ZMmnKWL19eFixYIOXKlZMbbrhB3n///TTr8yAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ+EvA8iXwvXv3VsbPHDlySOvWraVjx45eZUKweSyRHzZsmKxYscJrPR4gARIgARIgARIgARIgARIgARIgARIgARIgARIggUAIWO4BOmfOHKlVq5Zgmy9fPvnpp5/kiy++8CobkiVcvXpVJk6cKPXq1fNajwdIwF8C165dk/Pnz0v27Nn9PZX1SSDiBDB2kdQASeJYSMAkApcvX1a/68zmav+rdvz4cUHM9uQFczKs4omOjk6+WzJkyKAyTGfKlCnFfid94bzBSVfTfbpw3uC+a+4UjTlvcMqVdKceyG9De4M5195SA+j+/fvVRHrEiBHK+OkLBky+UdatW+dLddYhAZ8JbNmyReLj46VBgwbMzuYzNVa0A4FTp07J/PnzpWTJklKjRg07iEQZSMBnAgsXLpQLFy5ImzZtaMD3mVr4K+7cuVOQeRfXyteCVT2TJk3ytbpx9TBnwNzhpptuUuGZjFOAAruWwOnTp2XevHlSvHhx5YjiWhBU3EgCS5YskbNnz6p5g5Nfshl5cSh0mgS2b98uGzduVI58RYsWTbMuD9qDgKUG0MKFCyvrd7Vq1XzWbunSparuiRMnfD6HFUnAFwJ4G4Oit76cwzokYAcCeszqrR1kogwk4CsBjFt4c1y5coUGUF+hRaBenjx51AuWgwcPpuj9wIEDavUE4rMn92iAB6jTX8joe67epgDDLyRgYwLw/kTh2LXxRaJoXgkkJibKpUuX1NyBBlCvmHjAhgQwdlF477XhxfEikqUG0MyZM0vNmjVlw4YNUr9+fS9d/rN76tSpMnv2bLWjTp06/xzgXyRAAiRAAiRAAiRAAiEjUKBAAVm2bNl17cNzd9asWfLVV1+pWO7XVeAOEiABEiABEiABEiABEjCQgOXB5Tp06CADBw5U3gNp8UBs0C5duiRVwXIjFhKwkoCOncg3iVZSZVvhIKDHrt6Go0/2QQJWEdDjVm+tapftkECoCegxq7eh7o/tk4BVBPRcl2PXKqJsJ5wE9LjV23D2zb5IIBgCeszqbTBt8dzwELDUAxQi9+zZUxYtWiRNmjSRrl27qqD52I/YNFu3bpXNmzfLqFGjRC99x7GWLVvKQw89hD9ZSMAyAuXLl1dJHIoUKWJZm2yIBMJBoGDBgoJQItiykIBpBLCiA0vg9QO5afJTXvcSiIuLk5w5c0pMTIx7IVBzIwkg8Wz16tUlf/78RspPod1NoHbt2ioedZYsWdwNgtobR6BcuXIqXFBsbKxxsrtVYMsNoIgRNW7cOLnnnnvkscceU1yxND51NlENHBnjJ0+eLKjDQgJWEsiRI4eUKVPGyibZFgmEhQDuo6VLlw5LX+yEBKwmUKhQIaubZHskEBYCiHlatmzZsPTFTkjASgKYN5QqVcrKJtkWCYSNAF/4hw01O7KYQFRUFOcNFjMNdXOWL4GHwLlz55Zff/1VZsyYod5GwhMkdalQoYJMmDBBVq5cKQjEz0ICJEACJEACJEACJEACJEACJEACJEACJEACJEACVhMIqdtl27ZtBR8sf9+yZYv89ddfUrRoUcHSZGSMZyEBEiABEiABEiABEiABEiABEiABEiABEiABEiCBUBIIqQFUCw6P0Lp166qP3qe3yDSKJRswirKQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgJUEwmIATUvgTZs2yeLFi+X1119PqxqPkQAJkAAJkAAJkAAJkAAJkICrCXz44YcyevRouXbtWhKHK1euyL59+1TOhbx58ybtxx+Irzh16lQmVkxBhV9IgARIgATcSMBvA+i2bdvk888/F2RpwwdZXjNmzKg+AIgg3Ph4+jv5Pvxo79mzR7777juVcZMGUIWM/1hIIDExURISEpSHMbMRWwiWTYWcAO6Pu3btEiSTgQc9CwmYRODQoUMqC3yxYsVMEpuykoCcO3dOGZGwMonJOTkg7Epg+fLlKrSYJ/kwhg8ePJji0I4dO+TIkSM0gKagwi92InD48GG5ePGixMTE2EksykIC6RLgvCFdRLar4LcBFA80w4cPV5NEK7VZunSpNGjQwMom2ZbLCcTHx8vu3bslW7ZsEhsb63IaVN8kAnhQWb9+vRQpUkTq169vkuiUlQTkjz/+UA8yt912m3pJSiQkYAoBvOTHy6esWbNKiRIlTBGbcrqMwFdffaVWzl29ejVJ8zlz5kiPHj2kUqVK8uOPPybtxx/58uVj7oUURPjFbgRWr14t58+fV+MUDlYsJGAKAbxgwgcvTfHylMX+BPw2gObMmVNatWol06ZNs1S7L7/8kgZQS4myMT0x1FsSIQFTCOgxq7emyE05SQAE9LjFlt73HBMmEUg+dk2Sm7K6iwAMRHFxcSmURkgxFLz0r1ChQopj/EICdifAe6/drxDl80aAY9cbGfvu99sAClVuueUW2blzp4waNeo6V3V4LXXs2FFeeOEFufnmm6VkyZJJy+NTY5g3b54MGjRIRo4cKdWrV099mN9JgARIgARIgARIgARIgARIgARIgARIgARIgARIICgCARlAW7ZsKX/99Zc0bNjwus47deok3377rdx5553XHUu949FHH1XxQrt27Spr165NfZjfSSAoAjly5FDn621QjfFkEggjgezZs6veOHbDCJ1dWUYA4xZL2ej9aRlSNhQmAvqeq7dh6pbdkEDQBOD5icL7btAo2UAECOCei/j3jL0cAfjsMigCer6gt0E1xpPDQiAgA2i5cuWkS5cu1wl46tQplfjAF+OnPrlDhw7y1FNPCWLX3H333Xo3tyQQNAEsAUIsjqioqKDbYgMkEE4C0dHR0rZtWxWHLpz9si8SsIJA48aN1YMMEiSykIBJBLCsGLE/OW8w6apRVhDQL05z5cpFICRgHAE4VcEASgO+cZfO9QLDLoZcI5w3mDMUAn46ufHGG6/TEkln/M36irgJly5dUgbQ6xrkDhIIgkCGDBl4MwqCH0+NLAF4c2AMs5CAaQTgwcEkBqZdNcoLApw3cByYToDzBtOvoDvl57zBndfdKVrT+GnWlQzYAOpJzcqVK6vsr56Oedu3aNEilTABmTdZSIAESIAESIAESIAESIAESIAESIAESIAESIAESMBKApYaQLH8IiYmRiU28kXIHTt2SLdu3VTVWrVq+XIK65AACZAACZAACZAACZAACZAACZAACZAACZAACZCAzwQsNYCi1wEDBsgrr7yi4nrCwOmpnD59WmV+R5ywv//+W1Vp0qSJp6rcRwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIBEwgoCVJavd1+++0yePBg6du3r4wePVrq1asnZcqUUbFBjx07Jrt375aVK1cKjKC6IKHSv//9b/2VWxIgARIgARIgARIgARIgARIgARIgARIgARIgARKwhIDlBlBI1adPH0ECj9dee02WL1+uPt6khcF01KhR3g5zPwkETCAhIUEQW7Z27dqSM2fOgNvhiSQQbgIXLlxQL4qQjRgfFhKwI4ElS5bIfffdJ+fPn08h3smTJ1U21zx58qRI5IXESCNGjJC77747RX1+IQG7EDh48KBs3bpVEJaJ2bTtclUohy8EkFAW5dy5c75UZx0SsBWBjRs3ytmzZ6Vu3bop5g22EpLCkIAHAocOHZItW7ZIjRo1JDo62kMN7rIbAcuXwGsFn332WcES+Oeee04qVKggyO6mS9asWaV+/fry66+/yrRp05SxVB/jlgSsIgAD6PHjx+Xo0aNWNcl2SCAsBE6cOKHG7b59+8LSHzshgUAI4N6KMXrkyJEUHzyIX758WY3h5MdwTz5w4EAgXfEcEggLAT1vwLhlIQGTCMB4hIIXqCwkYBqBPXv2CO6/Fy9eNE10yutyApjXwt7AeYM5A+Efq2QIZM6XL58MGTJEffBABINoxowZpWzZspIpU6YQ9MgmSYAESIAESIAEwkHgjjvuEHh7pn7gLl68uPIKhSdd/vz5k0TBi9C8efMmfecfJEACJEACJEACJEACJEACJBAuAiE1gCZXAkvfbrzxxuS7UvyNtz5FixZNsY9fSIAESIAESIAE7Esgd+7cgk/ykiFDBvW1QIECgg8LCZAACZAACZAACZAACTiNgJ7zOk0vJ+sTsiXw/kCDB8mnn37qzymsSwLpEihWrJjyPuIDeLqoWMFmBOAlV7BgQYmJibGZZBSHBNIngJUeKHjxyUICJhHQ8wbcf1lIwCQCOtZ9VFSUSWJTVhJQBEqWLKkSJiNMHgsJmESgSJEiyt5QqFAhk8R2taxh8wD1RvnatWvK+IktCwlYSQA3JHxYSMA0Akgi17BhQ9PEprwkoAgwxA0HgqkEChcuLPiwOI+AjpHp1DjE+oUTDaDOG7tu0KhSpUpuUJM6OpAADJ80fpp1YQMygL7yyivy22+/ycsvvyytWrVK0hiJO/DQfuXKlaR9af2BuKAIGHvq1Cl59dVX06rKYyRAAiRAAiRAAiRAAiRAAiTgN4GpU6eqc1atWiVXr15VOQn8boQnkAAJkAAJkAAJGE3AbwPounXr5I033lBKI8M7vuuCZZtYPjRnzhy9i1sSIAESIAESIAESIAESIAESiAiBLVu2yNy5c1XfyNY7ZswYefLJJyMiCzslARIgARIgARKIHAG/DaClS5eWPHnyqMyvNWrUuE7yhx56SBlAs2fProyhaS3FgAfo4cOHBZMRlrQJJCYmSo4cOdKuxKMkQAIkQAIkQAIkQAIkQAJJBHr16pVidRpWsN17770Cxw0WEiABEiABEiAB9xDw2wCKbK87d+6UtWvXStOmTa8jdeedd6r4SatXr/Y5gcd7770nOjbPdQ1yhyKwYcMGGTRokLRo0UIefPBByZcvH8mQAAmQAAmQAAmQAAmQAAl4IfDzzz/Lr7/+muIonC9ee+01+eCDD1Ls5xcSIAESIAESIAFnEwgoC3z+/PmVcdNT4qLo6Gjp0qWLz8ZP4H3kkUecTTmZdvB6XbRokYwYMUIZNBGTaPfu3clqeP6zXr16MnbsWDVZQ5gBeNqypE0A4/PcuXNpV+JRErApgfPnz6s4ZTYVj2KRgFcCnuYGXivzAAnYiADnDTa6GBaIgjl37969Pbb08ccfy9atWz0eM3kn778mXz33yn758mW5ePGiewFQc6MJ0N5g1uULyAD60ksvyY033pgiAVJytZ9++unkX9P9u2DBgtKhQ4d065leAV6xderUkSZNmggYDRgwQNq1ayelSpVSS3F27NiRpooFChSQ9u3bCwwj48ePT7MuD4og5tOsWbNUmAXyIAGTCCAx3MyZM1PEWDZJfsrqbgI6ESISjbCQgEkE4uPj1bzh4MGDJolNWb0Q+OijjwTX1FOBcRRL451SECoL5cyZM05RiXq4iMCSJUtUCD09f3CR6lTVcALbt29X84aEhATDNXGP+H4bQPFm8ZNPPlGEjh075pFUbGysx/1p7axSpUpah40/tmbNGrnpppu8GjS+++47qVixorzzzjtp6lqkSJE0j/PgPwT02xi9/ecI/yIBexPQY1Zv7S0tpSOBlAS0BxIfZFJy4Tf7E9D3XL21v8SU0BsBLHN//fXXvR1W+2fMmCG//PJLmnVMOai953jfNeWKUc7kBGDAx0sJeIKykIBJBPTLJ84bzLlqfhtAN2/erBIgNW7c+LqYOlAbS7V9WdJtDqLgJcUNHcv8sUXJkiWLdO3aVT777DPFEG+oH330URU7tV+/fnL//ffLhQsXPHaMc1lIgARIgARIgARIgARIgAQ8E+jfv796XvF89J+9WCKv5+f/7OVfJEACJEACJEACTiTgdxIkLNOuWbOmzJ07VxnyUkP5+++//Z5I4G3P+++/L3369EndnCO+L1u2TP7880+lS9GiRZWbdHKP1zZt2qhjeHMwdOhQ5QXaunVrQXxQJjsKfAhkzPg/+36mTJkCb4RnkkAECOixq7cREIFdkkDQBDh+g0bIBsJMQI9ZvQ1z9+zOIgJI1Pr555/71BrigCIuv+nL4fWYzZAhg096sxIJ2ImAHr96ayfZKAsJpEVAj1m9Tasuj9mDgN8eoCVKlJDSpUt7NH4GqhJiJ2j34UDbsPN5yOCuy8iRIyW58VPvxzZ79uwqLui2bdskKipKxQplPInkhPz7u3z58lK1alVh2AD/uLF25AkgLnK1atWkUqVKkReGEpCAnwT0Sye99fN0VieBiBGIi4tTc7SYmJiIycCOgyfw7LPP+pVEEBnhsWTe5JI7d24lfs6cOU1Wg7K7lEDt2rVVngyudHTpADBY7XLlyql5QyAhIA1W22jR/TaA4qH8r7/+ki+++EKOHDkSlPLw/NyzZ48MHDgwqHbsfvK+ffuUiNHR0XLHHXekKy4MdohLdOutt0qjRo0kveRI6Tbo0go5cuSQMmXKCB/CXToADFYbHhx40aQfaAxWhaK7kADfgrvwojtEZbyILlu2LOcNBl/P77//XhYuXOiXBidPnlQOCH6dZLPK2vMzc2a/F/fZTBOK40YCePHPF09uvPLm6wynNcwbeO8151r6bQDFDyyWq3fv3l0KFSokMDLlypUr6YPMmcgQj0GQ3idr1qxSsmRJmTRpkjnEApAUnogoMGjoCUp6zeABEgmRXnrpJbn55ptl48aN6Z3C4yRAAiRAAiRAAiRAAiTgSgLnz5+XF154ISDdEZdfh6sKqAGeRAIkQAIkQAIkYHsCfhtAoVHTpk1lwYIF0qBBA0HcyrNnzyZ9kP0VGQh9+ehMsbanFKSAyP4OgyaWtvubnfGxxx6TMWPGyG233SbIJM9CAiRAAiRAAiRAAiRAAiSQksB7770XcCLWq1evCpbOs5AACZAACZAACTiXQMDrJOrWrStLliwReHwiM/ymTZvUFm9QmzVrJr7EoEGmc2SMX79+vXMJ/59m8AB94IEHZPz48Sp5FBIc+VOQJAlesh07dhRwZyEBEiABEiABEiABEiABEvgfAYSbwsqpYAqcOyZPniwdOnQIphmeSwIkQAIkQAIkYFMCARtAtT433HCD4AOjJwomDh999JEgIKyvBdkXg40n6mtfkao3ePBgWb58uXTp0kVWr17td2KeevXqyfTp06VVq1aRUoH9kgAJkAAJkAAJkAAJkIDtCPTt21etRgtWMCyhx6orxHVjIQESIAESIAEScBaBoA2gVuB48MEH5cMPP7SiKdu2gcDOMIB26tRJeXFC33bt2qml8b4KjdiqixYtkpYtWzIxkg/QEhMTJSEhQUqVKsWEBj7wYhX7EEB4kF27dqk4y0yEZJ/rQkl8I4ClpCwkYCIBhHWCJyHmDUxoYNYVRG6Cbt26eRUayUXffPNNwaqqV155xWs9HMAKNdMMoDqsGBLMspCAaQQOHz4sFy9eZCIk0y4c5VXhIDlvMGsgWG4AHTVqlN/ejXny5JH27dubRS4AafPmzauyu69cuVK++eYbmTBhgkyZMsWvljAp10ZQv050YeX4+HgVYiFbtmwSGxvrQgJU2VQC8IhHaJAiRYpI/fr1TVWDcruUgI51rbcuxUC1DSSAWO14+YQknSVKlDBQA/eK/K9//StN5bdv366OFy5cWOUwSLOygQdPnTqlpEZeBhYSMI0AVkciiRn+f2bJksU08Smviwns2LFDOabhpSnsNCz2J2C5AfTOO+/0W+tLly7JgQMHpEqVKn6fa+IJiOMZTCzPokWLMlOlDxdeeyHprQ+nsAoJ2IKAHrN6awuhKAQJ+EmA49dPYKwecQJ6zOptxAWiACTgIwHtAaq3Pp7GaiRgCwL6nqu3thCKQpCADwT0mNVbH05hlQgTCCgLvNUyz5o1SxYvXmx1s45uj0uzHH15qRwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIBFBCz3AIVcsID//vvvKiv81q1bVWwEb/IigzyMny+++KK3KtyfjMAHH3wgw4YNk7Zt2wrCDbB4J5AjRw51UG+91+QRErAXgezZsyuBOHbtdV0ojW8EMmTIoCpmypTJtxNYiwRsQkDfc/XWJmJRDBJIlwDCPaHwvpsuKlawIQHcc+G9TAcfDuEg7QAAQABJREFUG14cipQmAT1f0Ns0K/OgLQhYbgBFEOP7779f5syZYwsFnSTEiRMnpF+/fipI9OjRo6Vz587SqFGjsKiIwNRWLKvR7uFoC/HhMmbMKPph2ZMiOoYc6qCut4L2dNu6zQoVKqhYHKkD2aOe1kXX9dZuMP17a5P9k3964y86Olq95MCDDMZgIOOf488zATv8/4NkGAP4hOr+51n7/72gTG/86XMDvf/pB3BP92w78A+1/mldU9P0d9v9Jy4uTsX+xLwh0PHvpOuPe4GTfn+gD/4P4to6bf6HuLUouXLlUltP/5h2/4EOThp/5O99/t+wYUM1J9LzB95/r6j/whz//j//e7r3YV+o/v+VLl1aEJ4wPQNoKPr3piv3p03AcgMojHI0fqYNPdCjMIogmzwC9CNAdNmyZQNtyq/zevfuLfA8tbLAM/i///2vulk0b97c4xvr5cuXq9iwut8yZcpI1apV9dekLYJmz5s3TxBLFgU/FgiGX6hQoeuyeP7999+CQNu64GYVyv51P3rL/snf1/G3du3aoMe/Hnd6a9L4QzZm3CN0CeT/vz5Xb+2g/5IlS5Q4yNSL30on3n+0gVFz11s78Pf1/58Vvz9ab701SX/IDAZ4+YnilP9/6V1/zB9g/HT79Xei/kePHlVjGdl6wzH/VJ0l+yfU//+ROBFFz4WTda3+DHX/6CT5/Nvq/lM7M6D9tOb/Vvfv7/MH+09JIL3xl9zz04n3n/T0T06L+gf//J+cJ/52Iv/UOvK77wQsNYDCQxGGKJR7771X7rrrLpXBOHfu3B4lwgPg/v37ZcCAAR6Pc2dKAnhbvWDBApk0aZK0bNlSvW1IWSM03/A2GUty8eYi2KI9SaELPvjBw4TJU8Ex1NFFvxXU3/UW56OufluIesnP0/WwxX4c1w/o7J/8Of7s+f9P/3/V/3+d8v9f35v0fcuJ409fs9Rb3n/N+f3R///0eHXK/z/+/rt3/pP8Xotx7bT5n9ZPb3n/TUmAvz/m/P7w+Y/Pv3rugf/FnH8wnFTKu3nw3zL8nyHoWvDN/K+FQ4cOKaNcnTp11NtzX9v9888/ZcqUKTJw4EBfT2E9QwnUqlVL1qxZo4zjuOYsJEACJJCcAOJCV6pUSSpWrCibNm1KfsgRf58+fVrgzY8Xg6dOnXKETqmVyJMnj9Lt5MmTStfUx03/XrlyZTU2N27cqMaq6fqklr9NmzaC5JQzZ86U1q1bpz7M7w4l8NZbb0n//v1VTH787bTy1VdfycMPPywPPvig4G+nlfnz56sVBTfffHOSM4qTdNy2bZuUL19eEKYiPj7eSaopXbDqBavS4HCSmJjoOP2oEAmQQPAE8Ps1YcIEGT9+vAqFGHyL7mzBUg/QwoULS7169QSxEPwp1atX9/scf9pnXRIgARIgARIgARIgARIgARIgARIgARIgARIgAXcS+Gd9sUX6Y+n77NmzBcvb/Snbt2/3pzrrkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEC6BCw3gD799NNSpUoVGTt2bLqd6wobNmyQadOm6a/ckoAlBBISEmThwoVy9uxZS9pjIyQQLgIXLlyQxYsXy549e8LVJfshAcsI6HjMljXIhkggTAQOHjyo5g1nzpwJU4/shgSsIaCTH2EpNQsJmEYAIWVWrFiRlKPBNPkpr3sJIAQk7A1ODWvlxCtr6RJ4JLhBjJY33nhD7rvvPuUFimXxaZXdu3erOAZImOS2ghgvv/zyi8D7de/evYLMlPjPU6BAASlYsKCUKlVKmjVrJrVr1/YaANhtzPzRFwbQ48ePCzJ/5syZ059TWZcEIkoACeUwbhH4u0SJEhGVhZ2TgL8EdMI8/UDu7/msTwKRIqDnDUeOHBEkgGQhAVMI6Jf9eIHKQgKmEcALf9gR8MmWLZtp4lNeFxM4cOCAsjdg3oAY/yz2J2CpARRvHZHkBjcvlO7du/tMwE0G0Hnz5smXX36pEj/54mWA/0wdOnSQvn37qgDgPkNlRRIgARIgARIgARIgARIgARIgARIgARIgARJwOQFLl8Aj82urVq1cjtS7+nCRbt++vbRo0UJ5vfpi/ERr8ApFSAFkRe7WrZvf8VW9S8QjJEACJEACJEACJEACJEACJEACJEACJEAC/hDIkCGDP9VZ1wYELPUAhT4w8P3888/KYxEZ4bNnz+5VzWvXrgnchSdMmOC1jlMOQM8mTZrI1q1blUpY1lq1alWJi4uT4sWLS44cOdQnKipK4El7+vRp9YHxE+cgLgqWyI8cOVJ27Ngh06dPl6xZszoFT0j0KFasmIr/iZACLCRgEoG8efOqMBgxMTEmiU1ZSUARyJjxf+9Ws2TJQiIkYBQBzBsw/0IYIhYSMImADvWE5wgWEjCNQMmSJdUzG59tTbtylLdIkSKC0GWFChUiDEMIWG4AvfPOO6VMmTIyceJE8fXhp3HjxirhhyHMAhLzscceU7E+e/ToIY8//rhUq1bN73b2798vb7/9tnz88ccyYsQI6d27t99tuOkE3JDwYSEB0wgg/lHDhg1NE5vy+kHAyYmCELuWhQRMJIC49enFrjdRL8rsfAL6mYsGUOdfaydqWKlSJSeqRZ1cQACGTxo/zbrQlhtA8+fPr4xz+ofYFxzNmzcXeDw5tWzevFlmzpwp8+fPFxh7Ay3wTBg+fLjcdtttKslUz549RXvaBNomzyMBEiABEggfgcuXL6vOmKgifMzZEwmQAAmQAAmQAAmQAAmQAAlYGgNU47zlllv0nym2WPLuqcCIV6dOHU+HHLFv+fLl8uSTTwZl/EwOonXr1mp51q5du5Lv5t8kQAIkQAI2J/Dpp58qCeEBOnnyZJtLS/FIgARIgARIgARIgARIgARIwBkEQmIA1WiwZPu5555TiZHgvYhlGYh72alTJ/n22291NcdvkfyocuXKlupZv3592bt3r6VtsjESIAESIIHQETh69KgKY6J7eOGFF+T8+fP6K7ckQAIkQAIkQAIkQAIkQAIkQAIhIhASAyg8PeHlgnge77//vsyZM0cSEhLk4sWLsmHDBmX8hBEUSYH+/PPPEKlmn2ZjY2Pljz/+sEwg8F27dq1Ur17dsjbZEAmQAAmQQGgJDBgwQAVK17389ddfMmTIEP2VWxIgARIgARIgARIgARIgARIggRARCIkBdOjQoWrJ98mTJ1OIjUznSEqjEyQsWrRIJfpAjEwnF3hrfvHFFzJ16lRL1HzttddUgiknx021AhQMxefOnbOiKbZBAmEnAM/Aq1evhr1fdhgaAuvXr5cxY8Zc1zgS2+3bt++6/Sbv8BbuxmSdKLs7CHDe4I7r7GQtef918tV1rm6Ijw5HKRYSMJEA7Q1mXTXLDaBbt26Vl19+WVHIly+fdO3aVRYsWCBHjhyRs2fPKk9QJH/YvXu38g6NiYmRDh06qGNmofNd2rJly8odd9wh7dq1k44dO8rcuXNFJ8LwtRXEi5s9e7a0adNGYAB94oknfD3VtfW2bNkis2bNksOHD7uWARU3k8CpU6dU4rR169aZqQClvo7As88+K54yv+N3sV+/ftfVN3mH1pMGfJOvojtlj4+PV/OGgwcPuhMAtTaWQGJiopL9zJkzxupAwd1LYMmSJWrFqJ4/uJcENTeNwPbt29W8AaudWcwgYHkW+FdeeUXFNHvooYdk9OjRKu5nahTwAC1RooT06tVLHn/8cbn99tvl448/lj59+qSu6pjvo0aNUsv/v//+e8EnT548Ki5ouXLlpFSpUpIzZ06Bh2z27NmVcRQPxZjMwDMIHrLwHjp27Jji0b9/f2VYdgycECmi38bobYi6YbMkYDkBPWb11vIO2GBYCfz4448yf/58r31+/fXX0r17d7npppu81jHpgPZA4oOMSVeNsoKAvufqLamQgCkEtPcc77umXDHKmZwAnnkvXbqknoH1StHkx/k3CdiVgH75xHmDXa/Q9XJZbgBdtWqV9O3bN0Wih+u7/WdPrly5ZMSIEfLqq6/+s9OBfxUsWFA9ACMb/PTp0wXhAZYuXao+vqoL42jv3r3lzTff9PUU1iMBEiABEoggAax4eP7559OUAAZDeIguW7ZMMmTIkGZdHiQBEiABEiABEiABEiABEiABEvCfgKVL4LHsAkkdnnnmGb8kQbIkJEdyeilatKhMmzZNZsyYIffff7/kzp3bJ5ULFSokAwcOVGEDaPz0CZmqlDHj/4Y33yT6zow17UFAj129tYdUlCIQAkgEuGvXrnRPXbFihXz11Vfp1jOpAsevSVeLsoKAHrN6SyokYAoBPWad9hLt+PHjMm7cOHnxxRfVpdi/f7/07NlThQnyN5yYKdfSjXLq8au3bmRAnc0koMes3pqphbukttQDFK6/cXFxUqxYMb8oIkM6lny7pbRt21bwQZKTOXPmyLZt21Rs1AMHDghi/8FQihAB+BQvXlzq1q2rlsa7hY9VepYvX16io6NV4i2r2mQ7JBAOAvAYr1atmmDLYi4BxAN66623fFYAD3jt27cXrIwwueiXTnprsi6U3V0EMIdFSCLEp2chAZMIaKcKjF8nFMSQfvfdd2Xw4MHq2UjrhOfFYcOGqU+FChXkww8/VM9U+ji3ZhKoXbu2YMVMlixZzFSAUruWAMIZYpVubGysaxmYprilBlB4KsL6jRgevt7AsPQP8TFr1aplGrug5Y2KipLbbrst6HbYgGcCiKlapkwZzwe5lwRsTAAeHKVLl7axhBTNFwJIbuRPQgptMPXHaOqLHOGuw7fg4SbO/qwigIcYJK5kIQHTCGjPz8yZLX20iwgGOIjcfffdasUcBGjZsqU0aNBAXn/9deUkggS7iJ2NxLu33nqrMpSmF2omIooE0KlbvVr5wj+AwcJTbEEA9hzOG2xxKXwWwtIl8OgVBif8QPlS4DF67733yueffy716tXz5RTWIQESIAESIAHbE8CS9vHjx/stp69L5v1umCeQAAmQAAmQgAEEHn30UWX8LFy4sFopN3v2bOncubOSHCskkHB306ZNKt8EDL8vvPCCTJw40QDNvIv422+/qYNwIlqzZo33ijxCAiRAAiQQFAHLDaBIgATvlZtvvllWr14tnrIRbty4Uf1YwViKjOil/i8Leo8ePYJShCeTAAmQAAmQgB0I6KRGOhu6PzJhCdhzzz3nzymsSwIkQAIkQAKOIPDzzz/LN998o0JYwSjYokULj3ohxAqeOUeOHKmO4zkS8UJNLHhWhhFXFyRFZCEBEiABEggNAcsNoI0bN5aXXnpJ8KOFeB7aLbhRo0aCWC1YXlSlShUZMmSIIOYlfsAmTJggefLkCY2GbJUESIAESIAEwkgAS/OQ0T3QMmXKFJk3b16gp/M8EiABEiABEjCSwKBBg5TcSPpasWLFdHV44oknpHXr1sr4qY2h6Z5kswqjR48WOAfpsmjRIvnuu+/0V25JgARIgAQsJGC5ARSyvfrqqypbH+KAIpbJzp07ZcmSJRIfH68S/2j54fn53//+Vxo2bKh3cUsCJEACJEACxhJAggbE/gy2IMutpxUUwbbL80mABEiABEjAjgQOHjyoXh4ihv9//vMfn0XUHpNTp071+Ry7VITXKpb066JzaMAjFKHiWEiABEiABKwlEBIDKAJwYxk8Ypi0a9dOZTLXYsMDFAmP4CWKt13Ihs5CAqEgkJiYKDt27KARIRRw2WZICWDpNF4cnT59OqT9sHHrCYwZM0bdc4oUKSKePohphoK4ZZ6O632HDx9WIWKslzD0LSJ7LwsJmEgABoft27erl/cmyk+Z3UtAh1wxOYnO5s2bBXrUrVtXrRj09Wo2adJEVUVcUNMKnIaOHj0qTZs2VaJjZWTNmjVlz5498t5775mmTsDyYs6zb9++gM/niSQQKQKcN0SKfOD9hjRVYOXKleXHH39U0p08eVLd4OH1yQyxgV8wnuk7AXgc7969W7JlyyaxsbG+n8iaJBBhAkeOHJH169crA1n9+vUjLA2794cAPDfx8VZg1I6OjhYkckDWdycW7bmqt07UkTo5k8C2bdtk165dkjVrVilRooQzlaRWjiRw6tQppRdWIZhajh07pkQvUKCAXyrg9xT/Z6H7xYsX1d9+NRChyjD4Ytk+jJ4wdiIhMF6ODhs2TGDUfeeddwQJodzwDIO8IefPnxe8JNZesBG6LOyWBPwiAGcrfOAACDsXi/0JhMQD1JPaiPGJpEfa+AnvPJPfUnrSkfvsRUB7IemtvaSjNCTgnYAes3rrvSaPkIB9CTht/MJ4jSWW8NZBwd8wlrE4h4Aes3rrHM2oidMJaA9QvTVR30KFCimxsRTen3LixAll+MydO7cxxk/oh5eleBZ+8sknVX4MrTPyaXTs2FHwrIxET24o+p6rt27QmTo6g4Aes3rrDK2crYXfBlC8WUOsFcRmuf/+++XOO++UVq1aSYMGDaRGjRo+u69/+eWXgsDVLCRAAiRAAiRAAiRgVwLw1H388celYMGCKqyPfjjv37+/erGLOOYLFy60q/iUiwRIgASMIICVg/CiWrlypWiPVl8Enzt3rqqG51BTyvTp02XWrFmSL18+ef31168TGx6hCBs3ceJEWbp06XXHuYMESIAESCAwAn4bQOGWDi/Ozz//XL755huZNm2aClh98803yw8//CAxMTE+SYK3XViePGDAAJ/qsxIJ+EsAQdRR9Nbf81mfBCJFAJNeFI7dSF0B9hsMASzhQ8GyPtPL4sWLpXr16vLZZ5+p2K7NmjWT/PnzK7XatGkjefPmVQ+n2P/GG2+Yrq7r5df3XL11PRACMIYAwj2hmHzfxb0V91I422AZuC8FHq86Vmb79u19OSXidaDfc889p+QYOHCgeFryjxAcSISEAscjkz17lRLp/IN7LmwMMICzkIBJBPR8QW9Nkt2tsvptAMWDDbwedEGMEsQwGTRokJQtW1bvTneLH2gYUOEJOnny5HTrswIJ+EugQoUKggdUeO2wkIBJBBAjEgniqlWrZpLYlJUEFAH9AK5D3piKZd26dXLLLbcIkjPg/+PWrVtl/vz5KjYvdHr//fdl//798tprr6kXw8jki5htLOYSiIuLU/MGnazMXE0oudsI6BeniIdpckFSIBQ8Vy5ZsiRdVeA9uXz5cilWrJjy1E/3BBtUGD58uCDecMWKFaVbt25eJcLyd8T/XLVqlXpe9lrRAQewkgIrSvX8wQEqUQWXEChXrpyaNyCJKYsZBPw2gEKtLVu2KO0Q03PKlCkBB2fGBBMPDPSaMGOwmCYljPVRUVGmiU15SUARgDeH9qQjEhIwiYATxi0SOHXq1EnOnDkjDz30kPz8888eX/LC6IB5zPfff6+MoC+99JKsXbvWpMtFWZMR4LwhGQz+aSQB0++/jRo1kl69esmFCxeUUWHcuHEevR8Rk7lHjx4CD0oYzb744gsjVs0cOnQo6bn3ww8/TNPjER5l+qUaflugs1MLPD+Z/MipV9f5etHeYNY1DsgAOm/ePPUjgwQAeilYoGojjiiSCPz666+BNsHzSIAESIAESIAESMAyAlihsmnTJsGb/TFjxiQlcPTWQbt27dRDO4Lgaw8mb3W5nwRIgARIwDsBLGl/7LHHVFb3Ll26SKVKlWTIkCHqBCQ8Qkzm0qVLy8cffyx4WQwjaevWrb03aKMjMGQivuntt9/uk8x4TkaejQMHDiivWBupQlFIgARIwEgCARlAN27cKIj5WbVq1aCVxlKNli1bytdffx10W2yABEiABEiABEiABIIlMGnSJNUEliBmzZrVp+ZefPFF5c2DF7r+JPDwqXFWIgESIAGXEIBHJ+Iujx8/XhALEysPP/30U6U9QpLg2NGjR6Vp06by+++/ywMPPGAEmdWrVytPVfymDB061GeZEQ8Vnr3wGN2xY4fP57EiCZAACZDA9QQCMoAibkndunWvby3APXiLFx8fH+DZPI0ESIAESIAESIAErCOwYsUK1RhigPpakMgCcyMkuFizZo2vp7EeCZAACZCABwKdO3dWBj9kecdyd5Q8efIob9ANGzbIb7/9JjVr1vRwpj13IZkRVgk888wzgnjDvpY6deoIPGERFkAnT/L1XNYjARIgARJISSBgA2jt2rVTthTENwSN3b59exAt8FQSIAESIAESIAESCJ4AHlDhZYQkTkis4U8pXry4qn7w4EF/TmNdEiABEiABDwQQG7J58+bKaIjDyB8BI2DlypU91LbvLqwqWLx4sZL/5Zdf9lvQt956S3Lnzi0//fSTwCDMQgIkQAIkEBgBvw2gly9fVjFZTp48GViPHs5CZtXjx4+rt2IeDnMXCQREICEhQRYuXKjGa0AN8CQSiBABvOXHRHnPnj0RkoDdkkDgBJBAyOQCw2fOnDnVnOTs2bN+qaKXvuNBlcU8AjBcY96A5FcsJGASgUuXLilxz507Z5LYrpAV16RPnz5KVxgyo6Oj/dYbzkL9+/dX5/Xs2VNM/51NDQDh9bDy4tq1a6kP8TsJ2JoAEpth3qDnf7YWlsIpApn95YA3cfny5ZNly5ZZFnNl1apVKpkSHjpYSMAqAjCAwrCOOEF4mGUhAVMIIMg/xi3iYCH+FQsJmEQAHpQo+oHcJNm1rFieiGXsmOsgTrkvBQ+kmM+glC9f3pdTWMdmBPS84ciRI4IY9SwkYAoB/bIGL1BZ7EXg/fffT3qhjTiew4cPv05A/bt5/vx5qV69+nXHsQPhVVCw/B9xUJ988kn13Qn/4IU/9MMHia1YSMAUAkhQBnsD5g2BvNwwRU8nyem3ARTKx8bGqocCK0DAkxRvfW688UYrmmMbJEACJEACJEACJBAUAWTohQF09OjRPhtAf/jhBzUBrlixopQtWzao/nkyCZAACZCAMwj89ddfSYrAeJlWgQfkunXr0qqijiVvM93KrEACJEACJJBEICADaIsWLeSDDz5Q2fkefPDBpMYC+eOJJ54QLKtv3LhxIKfzHBIgARIgARIgARKwlMBTTz2lsvROnjxZpk6dKnfddVea7WPptE5OobdpnsCDJEACJEACriDwySefSHrL1uH5Wb9+feX9qJPweYOD1ZgVKlTwdpj7SYAEwkggQ4YMYeyNXVlBICAD6B133KEMoDBe4gZcr169gGTBD8J3332nzvUn02pAnfEk1xFA8gosCUJmXhYSMIlA3rx5pWDBghITE2OS2JSVBBQBHc4mS5YsxhJBvLVBgwaph9b7779fxo4dK/fdd59HfZDEEQbSvXv3qpe5yNbLYiYBzBtOnz6t7r9makCp3UpAh3qKiopyKwLb6o3fwvSSNunYrfj9rFatmm11CZVgJUuWVM9sWbNmDVUXbJcEQkIA80WELitUqFBI2mej1hMIKOhms2bNpFGjRoK3VZj0w0PCn6DFiE/z7rvvSu/evZVGWC526623Wq8dW3Q1AdyQ4FmsJ4WuhkHljSKA+EcNGzZk/E+jrhqF1QQQu9YJ5dlnn5Wnn35a8GDaqVMnweqX8ePHq7kP9EOiMniKVqlSRYXyqVq1qmAZvFP0d8I19FcHZJjGvIHxP/0lx/qRJqBfONEAGukrwf4DIVCpUiWpW7eu0JsuEHo8J5IEYPjEvIHJLyN5FfzrOyADKLpAQGf82CJg/D333CM1atSQKVOmpJnJHUvd4fEJg2ffvn1FB+p+++23+cDg33VjbRIgARIgARIggRATQLKKTz/9VCVqnDdvnjz00EOyc+dO1SsSUIwaNUole3rkkUdk6dKl9AAI8fVg8yRAAiRAAiRAAiRAAiQQKIGAlsCjM7ylGTNmjOilXgjYfPfddyujKLIWly5dWn1y5MghWB4WHx8vu3btUvE+kwvbr18/wZJ6FhIgARIgARIgARKwG4HHH39cOnbsKN98843MmDFDZs6cqV7g1qlTR9q2bau8Q+G9wkICJEACJEACJEACJEACJGBfAgEbQKHSww8/rFzVe/XqJceOHVNaXrp0SXbs2KE+aamNAM5IFPDWW2+lVY3HSIAESIAESIAESCCiBPLkySNdu3ZVH8Ry27Rpk4wbN05o+IzoZWHnJEACJEACJEACJEACJOAzgYCXwOsesBxsy5Yt4k82eMS2W716tWDpO2N9aJLckgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJWE0gKA9QLQyCv3711VeCGFhIALBhwwb1OXr0qKqSPXt2ufHGG1UGvNatW0vnzp1p+NTwuA0ZASTmQqIujD8WEjCNAMYusmHqjNqmyU953UvAn6SI7qVEze1IgPMGO14VyuQPAd5//aHFunYhgDwhV69eVfNeu8hEOUjAVwJIlkl7g6+0Il/PEgOoVuPmm28WfHQ5cOCAnD17VsUC5UO8psJtuAjAMxmxZxs0aMDEFOGCzn4sIXDq1CmZP3++lCxZUiWYs6RRNkICYSJw5coV1RMeZlhIwCQCmDNg7nDTTTfJDTfcYJLoAcu6e/duGTJkiIpxi0aQ2Ov48eMqTFW5cuUCbpcnhpdAYmKi6vDMmTPh7Zi9kYAFBJYsWaJsBm3atGFiZAt4sonwEUCum40bN0q9evWkaNGi4euYPQVMIOgl8Gn1XKRIESlbtiw9mNKCxGMhI4C3MSh6G7KO2DAJWExAj1m9tbh5NkcCISWgPZC0ITSknbFxErCQgL7n6q2FTduyqR9//FEqVqwoI0aMEL1qC8ZPGEER6/brr7+2pdwU6noCFy9eVDt5372eDffYnwAM+MgjAk9QFhIwiYB++eSWeYNJ18abrCE1gHrrlPtJgARIgARIgARIgARIgAQiQ2DFihVy3333eX1JDIMa4vwvWLAgMgKyVxIgARIgARIgARKwmAANoBYDZXP2IaDDLmTKlMk+QlESEvCBgB67euvDKaxCArYjwPFru0tCgdIhoMes3qZT3ejDzz//vPK4SksJhLHo1atXWlV4zCYE9JhlclmbXBCK4RcBPX711q+TWZkEIkhAj1m9jaAo7NpHApbGAPWxT1YjgbAQKF++vERHRwtCMbCQgEkEChYsKNWqVRNsWUjANAL6pZPemiY/5XUvgbi4OMmZM6fExMQ4GkJCQoIsWrTIJx3XrFkjiHHGeKA+4YpYpdy5c6u+MX5ZSMA0ArVr15YLFy5IlixZTBOd8rqcAH4bkQApNjbW5STMUZ8GUHOuFSX1k0COHDmkTJkyfp7F6iQQeQLw4ChdunTkBaEEJBAAAb4FDwAaT7EFATzEIHa90wsMmv6Ubdu20QDqD7AQ1kXSKiTrSl3Wrl2rdiGJ4uzZs1MflsyZM6dIVHtdBe4ggQgS4Av/CMJn10ERiIqKcsW8IShINjuZBlCbXRCKQwIkQAIkQAIkQAIkQAKhIoAHNn+Kv/X9aZt1/SPw3XffSZ8+fbye9Oeff0rr1q2vO44VUSdPnrxuP3eQAAmQAAmQgJsIMAaom642dSUBEiABEiABEiABEnA1AWR499WoiVAWNWrUcDUvKk8CJEACJEACJOAMAjSAOuM6UgsSIAESIAESIAESIAESSJcAQgQhw7svBZni8+XL50tV1iEBEiABEiABEiABWxOgAdTWl4fCkQAJkAAJkAAJkAAJkIC1BAYPHpxu3DIkgxo6dKi1HbM1EiABEiABEiABEogQARpAIwSe3YaeQGJiouzYsUOuXLkS+s7YAwlYSODatWuyc+dOOX36tIWtsikSCA+Bq1evhqcj9kICFhM4d+6cynh++fJli1u2X3P58+eXhQsXSrNmzTwK969//UuWLFkiN9xwg8fj3EkCJEACVhE4fPiw7Nu3z6rm2A4JhI2Am+YNYYMa4o5CbgCdNGmS9OjRQ9q3by/9+vWTiRMnyokTJ0KsFpsnAVFZMjds2CAJCQnEQQJGEThy5IisX79eNm3aZJTcFJYEQEC/dNJbUiEBUwgg2/nGjRtl//79pogclJzFihWT+fPnpzCEwvA5d+5cWbp0qZQsWTKo9nkyCZAACfhCYPXq1bJq1Sq5dOmSL9VZhwRsQwDOVpg37N271zYyUZC0CYTUANqtWzd57733JC4uTvr37y/16tWTzZs3S4MGDWTAgAFy9OjRtKXjURIIgoD2QtLbIJriqSQQVgJ6zOptWDtnZyRgEQGOX4tAspmwEdBjVm/D1nGEO2rcuLG0atVKSQGP0ObNm0dYInZPAiTgJgL6nqu3btKduppNQI9ZvTVbG3dInzlUau7atUvGjh0r2BYtWlR1U6tWLbn77ruV8XPcuHHSsGFDGT9+vNStWzdUYrBdEiABEiABEiABEiABEiABEiABEiABEiABEiABFxMImQcolm/CsKmNn8kZZ8uWTZ544gn55ZdfpHv37rJ48eLkh/k3CVhCAFlOUfTWkkbZCAmEgUD27NlVLxy7YYDNLiwnkCFDBtVmpkyZLG+bDZJAKAnoe67ehrIvtk0CJEACJPA/ArjnZsmSRTJnDplvFlGTQEgI6PmC3oakEzZqKQG/7zIXLlxQ8b3Su8hlypSRZcuWqfiLnoyg0AJ1ZsyYIbVr11YJPzJmDJk91lJobMwMAhUqVJBSpUpJVFSUGQJTShL4/wSio6Olbdu2kjVrVjIhAeMIaMMnf9ONu3SuFxghm0qUKMF5g+tHAgGQAAmEkwBWhSIBqJ4/hLNv9kUCwRAoV66cxMbGct4QDMQwn+u3AfTAgQNq6fqgQYPkoYceEu3pkVruypUrCx7iH3vsMZkwYYIg26SnUqBAAbnpppsEwY/r1KnjqQr3kUBABDA2afwMCB1PsgEBeMqzkIBdCSBJDOYAnkpiYqLafccdd3j05hg6dKhUr17d06ncRwIRJcB5Q0Txs3MSIAGXEqDnp0svvEPUpr3BrAvptwEUb8Zr1KghXbp0kWHDhsmQIUM8BkvHJBLL3N9++231oPP5559L69atPdLJkyePzJs3jwZQj3S4kwRIgARIgATsRQBGTmSKTqssWLDA4+Fjx4553M+dJEACJEACJEACJEACJEACJBAqAn6vOYdhEx6dMIQiTl2LFi0EXh5btmy5TsY333xTHd+7d6+0adNGqlWrJiNHjpSDBw8m1f3zzz/l+++/l9y5cyft4x8kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYAUBvw2g6DRv3rzy9NNPKy9QxPBEpveqVatKjx495MiRI0lyIY7HpEmTpH79+mofEiN169ZNihQpIlj6jlhL8CY9d+6cineXdCL/IAESIAESIAESIAESIAESIAESIAESIAESIAESIAELCARkAEW/HTt2lJ9++kkZLteuXSuffPKJ/PDDD4JAsO+++64gWRIKDJ1Lly6V999/P0U2biyB2759u4rR+PHHH0vp0qVVff5DAiRAAiRAAiRAAiRAAiRAAiSQkgDCi+3cufO6z9dff60qIq+Cp+Pr1q1L2RC/kQAJkAAJkIALCfgdA1QzQrarw4cPq6/w9Hz88celU6dOyvj52muvKYPo4MGD1T5kgu3Vq5d07txZfv/9d1m1apUcPXpUGUuxfL5s2bK6WW5JwDICCQkJsm3bNqldu7bkzJnTsnbZEAmEmgBeIK1cuVKFGkG4ERYSIAESIIHQE0CIpq1bt0qtWrUkV65coe+QPZCAnwSQNwGf1AXzXV3oVKJJcGsKgY0bN8rZs2elbt26XhMsm6IL5XQXgUOHDqlQkFjVjATgLPYnELAHKIyaiAGavGCy+Prrr0t8fLw0a9ZMGTyx/H3JkiWqWqFChVS8UNSB1yeMojR+JifIv60kAAPo8ePHlbHdynbZFgmEmsCJEyfUuN23b1+ou2L7JEACJEAC/5+AnjckD+dEOCRgAgEYj1D0CjwTZKaMJKAJ7NmzR3D/vXjxot7FLQkYQeDAgQPK3sB5gxGXSwkZsAF0x44d6u34woULZcWKFXLmzJkkrWNiYuTLL7+UP/74Q9Vp1KiRdOjQQXAOCwmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmEi0DAS+Bffvll+euvv1SSo5MnT6qlxkhmdO+998qzzz6rDJ9wBZ47d678/PPP8sILL0ilSpWke/fugnPz5csXLh1t209iYqL88ssvKhbq3r17Bd5ep06dUnFTCxYsKKVKlVKetFjCjTADLCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAApElkCFDhsgKwN79JhCQAXTy5Mny448/qqXuyePTwQsUyZAQgPuZZ54RBOpG+fe//y1t2rSRMWPGyMCBA2XcuHHKCApjaJYsWfwW2vQT5s2bpzxkp0yZksJz1pteiCcBD9q+fftK+fLlvVXj/lQEihUrpuLJIBEXCwmYRCBv3ryClyDwpmchARIgARIIDwHMG06fPq3uv+Hpkb2QgDUEdKz7qKgoaxpkKyQQRgIlS5ZUz2xZs2YNY6/sigSCJ1CkSBFB6DKEemQxg0BAS+AXLVokPXr0UAk6kquJGKB9+vSRZcuWqYzwyPyuS+bMmeWpp55S3o5PPvmkvPjii8ojFIZUtxQEyW3fvr20aNFCxo8f75PxE2zgFTp27FipWLGidOvWTS5fvuwWZEHpiRtS48aNmQApKIo8ORIEsmXLJg0bNrzuHhsJWdgnCZAACbiFQOHChdW8gQmQ3HLFnaOndiihAdQ519RNmmCVKBMguemKO0dXGD5hb8idO7dzlHK4JgF5gMIAl5ZXHSaO06ZNU8a+/PnzS5cuXZIwYnC89dZbyhj60ksvKc9GPOjDWIobn1MLAuM2adJEZReFjvCcrVq1qsTFxUnx4sUlR44c6oOJC0IJwAMBHxg/kZEUcVaxRH7kyJEqlur06dOFb8mcOlqoFwmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgFUEAjKAwpCHJe4PPfSQilPpSRh4ML355pvyyCOPpDCA6row+sELsmfPnvLcc89J8+bNlcFPH3fa9rHHHlPer/Ccffzxx6VatWp+q7h//355++235eOPP5YRI0ZI7969/W6DJ5AACZAACZAACZAACZAACZAACZAACZAACZCAmwgEtAS+adOmcvz4calVq5b897//9coLSZASEhIE3o/eChL8/Pbbb/L99997q2L8/s2bN8vMmTNl/vz58tFHHwVk/AQExKYaPny4zJgxQxmXr169ajwbKkACJEACJEACJEACJEACJEACJEACJEACJEACoSQQkAcoYiuOGjVK4NV4xx13yO233y7PP/+8in+QXFgY/g4ePCh79uxJN6B827Ztk5/qqL+XL18uiHuK+BBWlNatWyueu3btkrJly1rRJNsgARIgARIgAZ8JINTNPffc47E+EvwhVM5dd93lMdEh4iyykAAJkAAJkAAJkAAJkAAJkEA4CQRkAIWAjz76qGzatEmGDh2q4n0i5mf16tWlfv36KrkREv589913ggzmlStXDqdOtusLLKxmAM579+6lATSNq33t2jU5f/68ZM+ePY1aPEQC9iSAsYs4vxkzBuSob0+lKJVjCOBFKH7jPRX87iOG9bhx49QcwFMd7iMBOxLgvMGOV4Uy+UMAY5iFBEwjgJemWNnI/BamXTnKCwLI30J7gzljIWADKFQcMmSISuQD708sc//zzz/VJ7n6n332mSAeqJtLbGysLFiwwDIEmNysXbtWGZwta9SBDW3ZskXi4+OlQYMGggxtLCRgCgEkP0PIjJIlSwpCibCQgEkErly5osRlmBaTrhplBQHMGTB3uOmmm+SGG24gFBIwhkBiYqKS9cyZM8bITEFJQBNYsmSJnD17Vtq0aSOZMmXSu7klAdsT2L59u2zcuFHq1asnRYsWtb28FFAkaNeihx9+WE0W+/Xrp5Z4Y1kcPvBQ/OWXX9QyebeDBosvvvhCpk6dagmK1157TS0rzJs3ryXtObURvI1B0Vun6km9nEdAj1m9dZ6G1MjJBLQHkjaEOllX6uYsAvqeq7fO0o7aOJnAxYsXlXq87zr5KjtXNxjwL126pMLnOFdLauZEAvrlE+cN5lzdoDxAtZoFChSQwYMHq6/w+OCSTU3mf1vE6USs1Hbt2qmYaYgHikRSmTP7jh8Tmnnz5imv21mzZsnIkSNTdsJvJEACJEACJEACJEACJEACJEACJEACJEACJEAC1xHw3QJ33amed9D46ZkLkkZt2LBBZbtHxvs8efKouKDlypWTUqVKSc6cOSVHjhwqfgTioGAZAN4o7Nu3T5BMav369XLs2DHVeP/+/aVr166eO+LeJAJ6LHIpRRIS/mEIAT129dYQsSkmCaQgwPGbAge/GEBAj1m9NUBkikgCioAesxkyZCAREjCOgB6/emucAhTYtQT0mNVb14IwSHHLDaAG6R5WUQsWLKhi+sH7c/r06XLy5ElZunSp+vgqCILr9u7dW958801fT3F1vfLly6sEHEjWwUICJhHA/aJatWqCLQsJmEZAv3TSW9Pkp7zuJRAXF6deSMfExLgXAjU3kkDu3LmV3HCoYCEB0wjUrl1bLly4oEK8mSY75XU3ATizwUaDnC8sZhCgATSM1wmBcadNmya//vqrjB8/XhlCkSk3vYIEPt27d5du3boxmU96sJIdh0dtmTJlku3hnyRgBgF4cJQuXdoMYS2W8u+//1Ytwgv+xIkTwljHFgMOQ3N8Cx4GyOwiJATwEIOwRSwkYBoB7fnpT3gt03SkvM4lwBf+zr22TtcsKiqK8wbDLrLfBtCVK1fKsGHDBJ6MjRs3TlNdPMAuXLhQ/vjjD5UdHje3WrVqJX30j3WajTjwYNu2bQWf8+fPy5w5c2Tbtm2SkJAgBw4cEGR/hqG0RIkS6lO8eHGpW7euerPgQBRUiQRIgAQUAfxWwMMdvxcoe/bsUR6w9913nwwdOpQZmRUV/kMCJEACJEACJEACJEACJEACJBAIAb8NoDDGHTx4UJo0aSLt27eXyZMne+z3t99+k0ceeUT++usvj8dvueUWlRn9hhtu8HjcDTvxxuC2225zg6rUkQRIgAS8Evjmm2/kwQcflNTZa/H966+/FvyeLFmyREqWLOm1DR4gARIgARIgARIgARIgARIgARIgAW8EMno7kNZ+vSRx48aNHqu9+uqr0rx58xTGT8Szu/fee5VRtEGDBrJo0SIV4w4ekCy+E/jggw9U0iQmQfKdGWuSAAnYl8DOnTulS5cu1xk/k0uMZHDwBGUhARIgARIgARIgARIgARIgARIggUAI+O0BmryTa9euJf+q/v7999/ljTfeEH0MS96HDx8uDRs2TFH36NGjAkPp/fffLzCkIs4lS9oEEA+vX79+cvHiRRk9erR07txZGjVqlPZJFhxFwqbly5cnXdNgmsQSfxTE+Zs0aZJkzZpVEKvTU4GeiYmJSYdy5col3mIbIdzCpUuXVF3En0MweE8hFq5evarCDOhG2T/5c/xF9v/f559/ru5p+v+kt+2yZcvk7bfflpo1a15XxaT//1hBgYL71ZQpUxx5/7t8+bLSES84kyfkcMr998yZM0q/xYsXq98yk8af/g2GAt5+/44cOaL0g+f18ePH1d/8/XX+/CM+Pl5da7yUQrJOp82//vzzT6UfVqY5cf6pnVLwfDVz5kyla/J/TL//6t9O3G+T6+eU+y8SAKFg5YsT//+ZPv74/OnZZOSU/3/J75V2tj/s378/uaj8O0ACGf7PUHm9FTOdxu655x619L1ChQqyZcuWpNq4uWGJ/OrVq9W+OnXqqBiXefLkSaqT+g94NK5Zs0a++uqr1If4PRUB8EWmsV27dqksebt371bxQlNVs/xrhw4d5IcffrC8XTZIAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQPgEk04YjHEtgBDyb8wNrSz777LMk42e2bNnk+++/l7SMn+imV69earn8qlWrBAZTFu8E4Nm4YMEC9ea6ZcuWYTF+QppOnTopTwAYYIMtS5cuFWS+h4dmxYoVlUcnPEs8eWvC+0C/EcVxxEzFJ3WBDR9t6viBqIs2cT4STUVHRyedB68rvK3Sdn+80QtV/57eFrJ/8vdl/OHtIzy+MZYxjgIZ/6aMvxUrVoj2GEz9fzv195iYGKlSpUqK3d7+/9tVf3jg4fcO9/ObbrrJkfcfJEvE7wVC4WTJkiXpejnl/gfPT/w/xsoWeLh6+v2x6/jz5f6D8QkvMrzkxhzOyfef1L//uJbnzp0TzGH1nMct+sPzE0k5S5cuLbGxsUHNv+w4/pFYb/PmzVKgQAGVsRcypr7++mYV7PwzEvpjtdbWrVvV/BqhxlIX0++/mOPj3otVOzoJr2m//2ndf6EfVk1gboDflmCefyIx/jDecK/E812mTJlSDz+16iUt/bHyAPdcJEzGs1ug+gfaf3KBg/n/z/4Du/4m88czG8Yu/t9hzhTI+PdV/3Xr1qnE2cnr82//CVhqAP3yyy+TJOjRo4eUKlUq6Xtaf7Ru3VpgGKMBNC1K/zuGrPDPP/98+hUtrIFkV/hYURASAR6/LVq0UMs/rWjTWxvwRMZSeyyZLVGihLdq3E8CtiOApV5Y8l24cGH517/+ZTv5rBToxhtvVA9tvrT58MMPy6BBg3ypats6MLDgpQwMZ0js5MSCB1QYkb777jtlbHCajpUrV5ZNmzbJp59+KpUqVXKaetKmTRuZNWuWCl+E+Zmbytq1awWra6pXr+7zHNYpfN566y3p37+/ireMv51WsNIMvyG33nqrI1edIaTK3XffrVaK/frrr067fMo4X758ecGLUCfqh99M/Hbi5cvChQsdd/3SU2jGjBkqHFLbtm0Vg/Tq8zgJ2IUAjJJYnVu1alUpU6ZMSMVCwtgJEyaEtA83NB5QEiRPYHDjhteHLk8++aT+M90t3nbp2DXpVmYFEiABEiABxxDwx8DiT13HAKIiJEACJEACJEACJEACJEACJEACQRMIyAAK7z1kgod33fvvv6+WgsH6rZcxYoliXFycT8LBxR0JfWgA9QkXK/lBQCe30Vs/TmVVEogogezZs6v+3TB2n3vuOdH6pgW9SZMm0rRp07Sq8JhNCGAZHIqnZUA2EZFikIBHAvqeq7ceK3EnCdiQADwHUXjfteHFoUjpEsA9FyFzPC3fT/dkViCBCBLQ8wW9jaAo7NpHAgEtge/atas88MADKubnhx9+KIMHDxYsbdbl5ptv1n+mux0yZIgg7hCWerKQgJUEEL8MYRg8xQ21sh+2RQJWE8ASaSwDQpZmp5eSJUuq5Rz33XefihHlSV8sKZk4caKnQ9xnQwL6ARyrO1hIwCQCeHmPkDmcN5h01SgrCOgXiYhrykICphFA3FPEdNXzB9Pkp7zuJYAE1YibzXmDOWMgIAMo1EOQXyQwevrpp1VSHhgydfE13iKC7MN4ioKHYLcVBFn+5ZdfZPv27bJ3717Zt2+fIEEGArQjCDSMd82aNZPatWvzByGAwaETFwRwKk8hgYgT0N4cERckDAIgbhmSG8AbFFtdwADxbt5++21HxpLUejptqz1AnaYX9XE+Ac4bnH+Nna4h779Ov8LO1I+en868rm7RisZPs650wAZQrSZuWPAGxQfZ62AI9TUALLL/IiMcSnIPUt22U7fz5s0TJIxCwPIzZ86kqya8wTp06CB9+/YVBABnIQESIAGnEahXr54sWrRIFixYoF784AXQhg0bVLIgp+lKfUiABEiABEiABEiABEiABEiABMJLIGgDaHJxW7ZsKfj4WvDAiwzA8P5E1ninl0OHDslTTz0lP/74o1+qwit07NixymiK5FLDhw9njBS/CLIyCZCAKQR0OBQs50OmdBYSIAESIAESIAESIAESIAESIAESCJaApQZQf4XBUu/Nmzf7e5qR9Y8cOSJI4rF161YlP8IEVK1aVSWLKl68uCBwLj5woT537pycPn1afWD8xDnwlsUS+ZEjR8qOHTtk+vTprogPaOTFptAkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK2IRBRA6htKIRBkMcee0zF+oSn6+OPPy7VqlXzu9f9+/erWHgff/yxjBgxQnr37u13GzyBBEiABEiABEiABEiABEiABEiABEiABEiABNxEwDID6Jo1a2TVqlWybds29UFs0CJFiqiYla1atVJL3d0ENrmu8HKdOXOmzJ8/Xxo3bpz8kF9/FytWTC1/v+222wQZk3v27CnMsusdYUJCghqLSCLFpbTeOfGI/QhcuHBBVq5cqbIR+5pUzn5aUCK3Erhy5YpbVafehhM4ePCgWnWDuPTMpm34xXSZ+JcuXVIaYxUZCwmYRmDjxo0qL0jdunWFibxMu3rulhchDrds2SI1atQQ5G1hsT+BoA2gSFjx5ptvqgRIaalbunRpeeaZZ5T3o9uMUcuXLxfE7gzG+JmcbevWrVWW+F27dknZsmWTH+LfyQjAAHr8+HE5evQoDaDJuPBP+xM4ceKEGreZMmVSRlD7S0wJSeAfAlevXlVf9AP5P0f4FwnYm4CeNyBsEQ2g9r5WlC4lAZ1UFi9QWUjANAJ79uyRixcvqk+2bNlME5/yupjAgQMHlL0B8wYaQM0YCBkDFfPatWuCZd3NmjVL1/iJPmCs69Wrl0p49PXXXwfarZHn4c1A5cqVLZW9fv36snfvXkvbZGMkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4DQCAXuA9uvXT2UmB5DY2Fhp0aKFFC1aVC17z507t5w/f15OnjypYlUi2VF8fLzgrSS88Tp37iyzZ8+W0aNHixve8oAPPGWtKjA+r127VqpXr25Vk2yHBEiABEiABEiABATJF1EYyoCDgQRIgARIgARIgAS8E2DIBu9s7HokIAPoDz/8IO+++640aNBA3nrrLbW021ssStQZMGCAYFnRxIkT5ZNPPpFNmzbJuHHjlEEU+5w+cOCt2aVLF7nlllvkrrvuCnosvPbaa5IlSxbJmzdv0G05uQHETMWSIBjgWUjAJAL4v12wYEGJiYkxSWzKSgKKgJ4P4HeKxSwCeHmNF6wokydPVvMWszQITlrMG06fPq3uv8G1xLNJILwEdHixqKio8HbM3kjAAgIlS5ZUz2xZs2a1oDU2QQLhI4CcNwhdVqhQofB1yp6CIhDQEvhff/1V7rzzTpk7d640bdo0zUQ8OA7j5/bt26V79+6CZEnIhI7y7bffKuNoUBoYcDLidN5xxx3Srl076dixo+J2+fJlvySHJwa8Ztu0aSMwgD7xxBN+ne/GyrghIe6qnhS6kQF1NpMAPOMbNmzI+J9mXj7XS43YtSxmEnjvvffUCh5I/8033whiW7mpFC5cWM0bGP/TTVfdGbrqF040gDrjerpNi0qVKgkTILntqjtDXxg+YW/ACmgWMwgE5AG6dOlS+f7778XXH1lkgR82bJhMmDBB8Gbno48+UoZTxBCFB2n79u0FGTedXEaNGiUbNmxQ3MAuT548Ki5ouXLlpFSpUspIlyNHDsmePbvAOArPxcTERNm3b58gi/z69evl2LFjClH//v2la9euTsZF3UiABEiABEiABMJIAPONd955J6lHZJN+6aWXksIdJR3gHyRAAiRAAiRAAiRAAiRgIAG/DaDI7orM2nhT42vBW0kYTZOXDh06SM2aNQXLw9944w2ZMmVK8sOO+xvLWefPn6+ywU+fPl3FRwWT1FzSUhzG0d69e8ubb76ZVjUeIwESIAESIAESIAG/CPTt21e9fL3hhhvk4MGDkjlzZvnyyy/V6p3atWv71RYrkwAJkAAJkAAJkAAJkIDdCPi9BB6xvWDMQ6wDX8tvv/2mJtWp62Np+ODBg+Wnn35S3pGpjzvtO5JETZs2TWbMmCH333+/z67ScK0eOHCg7N69m8ZPpw0K6kMCJEACJEACESbw+++/qzjtWNlTvnx5JQ3C9iDp4rPPPhth6dg9CZAACZAACZAACZAACWWJj+4AAEAASURBVARPwG8PUHSJ5erPP/+8jBkzJs0ERvAW7dOnj1q+jWXwnsp//vMf+eyzz2TSpElSpUoVT1Uct69t27aCD5INzJkzR7Zt26bipCLWFrKvwlBaokQJ9SlevLiKiQLvTxYSIAESIAESIAESsJKANnJii7ndihUrVPN4Ubtw4UJZsmSJigfaqVMnK7tlWyRAAiRAAiRAAiRAAiQQVgIBGUC7deumgr3CcIeERk2aNFGZr+AdeunSJdmzZ4/88ssv8vXXX8vy5cuVQkjo4akgA/yHH34oPXv2VEvhPdVx6j54Wtx2221OVS/ieuFhDkZmGo8jfikoQAAEMHYRM1ln1A6gCZ5CAhEhgHsvizkEvvrqK1m5cqXExMRIv3795O6771bCIy45Vuk8+uijguXxSH6JfU4unDc4+eq6Qzfef91xnZ2mJfJfwHGKWeCddmXdoQ9iptPeYM619nsJPFSrV6+eDB06VHkGIKs5sm0ja3H+/PnVFol9nnnmmSTjZ/Xq1ZUnqDcs//rXv1RcUWQ6ZyEBqwhs2bJFZs2aJYcPH7aqSbZDAmEhAE/wmTNnyrp168LSHzshASsJ6N9yPMyw2JvAmTNn5MUXX1RCIgFSzpw5UwjcpUsXQfzPv//+O0WCpBSVHPQlPj5ezRsQA5WFBEwigMSpKPg/zUICphHASgOsitTzB9Pkp7zuJbB9+3Y1b0hISHAvBMM0D8gACh3h+fnJJ5+oeKD4jjc3SI6U+s0jjJ8//PBDulbxFi1aCAYQCwlYRQBvY1D01qp22Q4JhJqAHrN6G+r+2D4JWElAzwP4IGMl1dC0NWjQIBWCBy+iseQ9dcEqnWHDhqnd7733nlrhk7qOk77re67eOkk36uJsAhcvXlQK8r7r7OvsVO1gwMcqUtgTWEjAJAL65RPnDeZctYANoFDxqaeeUvErX375ZUGMT8StxGQZMSybN2+uYnuuXr1akOwovYJM8KVLl06vGo+TAAmQAAmQAAmQAAkESWDnzp3ywQcfqHkbjJyYv3kqCGGE+J+Y3COuOwsJkAAJkAAJkAAJkAAJmEggoBigyRXNmzevvP7660m78OYxU6ZMSd99/QOZ5VlIwEoCOnZiIOPRSjnYFgn4S0CPXb3193zWJwE7EOD4tcNV8C4DEh5duHBBHn74YZVs0XtNUcvff/rpJ5WwEiuAGjVqlFZ1Y4/pMau3xipCwV1HQI9Zby8yXAeEChtFQI9fvTVKeArragJ6zOqtq2EYonxQHqCedKSxyRMV7osEgfLly0vVqlVVjNpI9M8+SSBQAnghVK1aNalUqVKgTfA8EogYAT0P0NuICcKOvRKYN2+eTJkyRXLlyqUSHXmt+P8PFC9eXCVCwtdnn31WJatI7xwTj8fFxUmVKlVUQigT5afM7iWQO3dupXzqOL7uJULNTSKAWNN16tSRLFmymCQ2ZSUBQe4bzBtiY2NJwxAClhtADdGbYrqAALLVlilTJiCPZBfgoYo2JgAPDoQE0Q80NhaVopHAdQT4Fvw6JLbagZU6PXv2VDL1799fhS3yRcAXXnhBhTpCaKOxY8f6copxdZDFFWGbaLw37tK5XmDt+Zk5c9CL+1zPkgDCTwAv/mNiYsLfMXskgSAJREVFqXkD771Bggzj6TSAhhE2uyIBEiABEiABEiCBSBIYPXq0rF+/Xr0g7NWrl8+iwDj47rvvqvownJ46dcrnc1mRBEiABEiABEiABEiABCJNgAbQSF8B9k8CJEACJEACJEACYSBw/PhxeeWVV1RPQ4YMkWzZsvnV67333qvifx46dEiQvJKFBEiABEiABEiABEiABEwhQAOoKVeKcpIACZAACZAACZBAEAQGDhwoR48elebNm0u7du0CagkZ4xHmYPjw4bJt27aA2uBJJEACJEACJEACJEACJBBuAgwUE27i7I8ESIAESIAESIAEwkxg69at8sknn6he9+7dK02bNvUoAZbHozz33HOSP39+j3UQ8yoxMVEQF3Tq1Kke63AnCZAACZAACZAACZAACdiJAA2gdroalMVSAng4S0hIkFKlSjGhgaVk2VioCVy7dk127dolhQoVYiKkUMNm+5YTuHr1quVtssHgCWzevFkuX76sGoqPjxd80iobNmxI67A6po2l6VY0pMK5c+dk3759at7AhAaGXDSKqQhg3oCi/4+rL/yHBAwhcPjwYbl48SITIRlyvSjmPwQ4b/iHhSl/0QBqypWinH4TwMPd7t27VYyz2NhYv8/nCSQQKQJHjhxRSUqKFCki9evXj5QY7JcEAiKALOMoehtQIzzJcgJ33XWXIIP76dOn02wbnp+rVq0SxAitW7dumnVvvPHGNI+bdhBL+vHyKWvWrCrjvWnyU173EtBJyc6ePeteCNTcWAL4bTp//rwULlxYsmTJYqweFNx9BHbs2CH44KUpnK5Y7E/AFgZQPCTNnj1b2rZta39ilNAYAtoLSW+NEZyCup6AHrN663ogBGAkAY5f+122mjVrpiuUXvZetWpVadKkSbr1nVRBj1m9dZJu1MXZBLQHqN46W1tq5zQC+p6rt07Tj/o4l4Aes3rrXE2do5ktkiDB+Lls2TLnUKUmJEACJEACJEACJEACJEACJEACJEACJEACJEACtiAQEg/QS5cuyR9//CGIH7Vx40Y5fvy4R2VR79ChQ7Jw4UJ58cUXPdbhThIIlECOHDnUqXobaDs8jwTCTSB79uyqS47dcJNnf1YQyJAhg2omU6ZMVjTHNkggbAT0PVdvw9YxOyKBIAlky5ZNtcD7bpAgeXpECOCeC+9lxl6OCH52GgQBPV/Q2yCa4qlhImC5AfTo0aPSvn17WbBgQZhUYDck4JlAhQoVVCwOZKtlIQGTCERHR6uQIIhDx0ICphHQD+AZM9pikYlp+ChvBAnExcWp2J+cN0TwIrDrgAjoF6e5cuUK6HyeRAKRJNCwYUNlANXzh0jKwr5JwB8C5cqVE+Qa4bzBH2qRrWu5AbRTp040fkb2mrL3/08AXki8GXE4mEpAe3OYKj/ldi8B7QHqXgLU3FQCnDeYeuUotybA+68mwa1JBOj5adLVoqypCdDe8P/YOw8wKYqnjRfykXM+4h2SJEhGgSOLgHKIKEEFJIgCSlYUA4KCoCBKVDAgCKKigCQJggQJkrPkqHDknON3b/vvdXZv727DbJjdt55nb2ZnerqrfzM711NTXeVIJLi/m2oAvXHjhvzxxx+qx61atZKYmBiVQRQZ3ZzJ7du35cSJEzJo0CBnu7mNBEiABEiABEiABEiABEiABEiABEiABEiABEiABLwiYKoB9NChQ3L9+nWVNXTSpEkuKZY5c2b56KOPZNy4cS6VZyESIAESIAESIAESIAESIAESIAESIAESIAESIAEScJWAqQG6IiMjBTG/HnzwQVfbV+UiIiKkc+fObh3DwiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQFAFTDaCIf9CsWTPZuXNnUu3G289kCfGQcAMJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkICXBEw1gEKXDz74QLZv3y6rVq1yWTXEAR0zZozL5VmQBFwhEBsbK8uXL5crV664UpxlSCBoCCCe8ooVK+TIkSNBoxMVIQFXCdy5c8fVoixHAkFFAONRjBsuX74cVHpRGRJIisCtW7dUkWvXriVVlPtJIOgI7NixQ9auXasywQedclSIBBIhcPLkSTVuuHjxYiKluCuYCJhuAC1UqJAsWLBATWnfv3+/igmKuKDOPpcuXRKU6dOnD294wXRVhIguMICeO3dOzpw5EyI9YjfChcD58+fVdXv06NFw6TL7GUIE7t69q3qjH8hDqGvsSogT0OOG06dPh3hP2b1QI6Bf9uMFKoUErEYAL/xx/71586bVVKe+YU7g+PHjyt7AcYN1LgS3kyBhenulSpWS7CH+ARcuXDjJcrpAv3799CqXJEACJEACJEACJEACJEACJEACJEACJEACJEACJGAKAbcNoMWLF5ecOXPKwYMHTVGAlZAACZAACZAACZAACZAACZAACZAACZAACZCAVQgkS5bMKqpSz/8RcNsAiuNiYmJUzM7KlStLVFSUeHriMT0OcRNWrlzJE0ICphPIkyePiv+ZLVs20+tmhSTgSwKZM2eW7NmzS968eX3ZDOsmAZ8Q0EkNU6RI4ZP6WSkJ+IoAxg0Iz4T7L4UErEQgXbp0Sl0kpKWQgNUIREZGqme2lClTWk116hvmBCIiIgShy3LkyBHmJKzTfY8MoA0bNpTdu3erWJ9mdHXWrFmyceNGM6piHSRgI4AbEj4UErAagVSpUkl0dLTV1Ka+JKAIJE+enCRIwJIEMMMJHwoJWI2AfuFEA6jVzhz1BYESJUoQBAlYkgAMnzR+WuvUeWQArVWrlpiZnOOxxx5jpm5rXTfUlgRIgARIgARIgARIgARIgARIgARIgARIgAQsQcCjLPDwTmrfvr3TDh46dEjcyUDYrVs3uXbtmjz77LNO6+NGEiABEiABEiABEiABEiABEiABEiABEiABEiABEvCUgEcG0MQaq1KlimzatCmxInb7WrduLU2aNJGbN2/abecXEiABEiABEiABEiABEiABEiABEiABEiABEiABEvCWgOkGUHcVqlChgkqCNHbsWHcPZXkSIAESIAESIAESIAESIAESIAESIAESIAESIAESSJRAQA2g586dk0GDBqkp84sXL05UUe4kAXcJ3Lt3T4VXcPc4lieBYCBw/fp1uXv3bjCoQh1IwC0CuPdSSMCKBDhusOJZo85GArz/Gmlw3SoEbt++zdmgVjlZ1DMeAYRzpFiHgEdJkIzda9GihUybNs226c6dO1KnTh35v//7t+pkyZKJsw8e7q9cuWI7LlOmTLZ1rpCAGQR27dole/bskapVqzI7mxlAWYffCFy8eFGWLFkikZGRUrZsWb+1y4ZIwAwCGAdAaMA3gybr8CcBjBkwdqhcubLkypXLn02zLRMJTJkyRebNmydGY+CBAwdUCytWrJBWrVrZtRYVFSXvv/++3HdfQP1C7HRy98vVq1fVIZcvX3b3UJYngYATWLlypbIL1K9fX5InTx5wfagACbhKYN++fbJjxw556KGHJHfu3K4exnIBJOC1AfTHH3+UESNGSO/eveXWrVuqK+5awdOlS5dgUqUAsmHTFiegr0O9tHh3qH4YEdDXrF6GUdfZ1RAgoI0O2hAaAl1iF8KEgL7n6mWYdDvkujl48GDZvn27034dPHhQ8DEKHDVeeeUVSz+86lwKvO8az2xwri9atEh69uxpe26GlvqFIRyEHnjgATvFkXz4yy+/VAYWux0h9AUGfNgR4AlKA2gIndgw6Ip++cRxg3VOttcGUHS1e/fuUrFiRYmJiZHz589LuXLlJGPGjIlSwFvW/PnzS5EiReTpp5+W4sWLJ1qeO0mABEiABEiABEiABEiABEggMQLTp0+XtWvX2hWBgWnr1q3quQOOF0YpUKCApY2fxr5wPfgJwFssIQM9XiDu3r07Xif27t0b0gbQeB3mBhIgARLwEQFTDKDQLTo6WubMmSP16tWTzz77TE0f8pHOrJYEXCKgpzLxTaJLuFgoiAjoa1cvg0g1qkICLhPg9esyKhYMEgL6mtXLIFGLarhJAM4V+IST6GsW3qyU4CYAx6FGjRrZeYBCY4QvwDNLmjRp7DqQOnVqFRLJbmOIfdHXr16GWPfYnRAmoK9ZvQzhroZM10wzgIIIjKAzZ8706C1qbGysR8eFzJlgR0wnULRoUeWJHBERYXrdrJAEfEkge/bsUrp0acGSQgJWI6BfOuml1fSnvuFLAEYzeAfmzZs3fCGw55YkkCFDBqW3o3erJTsTBkrff//9YdBL17tYoUIFlRQ5RYoUrh/EkiQQBAQKFy6sXlrky5cvCLShCq4QMD3ad926dd1+S3XhwgX54osvXNGXZUjAZQJp06YVDDD4EO4yMhYMEgLw4ChYsKDoB5ogUYtqkIBLBPgW3CVMLBSEBOB5VahQIY4bgvDcUKXECWjPT52ENvHS3EsCwUUAL/z54im4zgm1cY0APLQxbuC91zVewVDKdAOou51CrBMYP3XSBHePZ3kSIAESIAESIAESIAESIAESIAESIAESIAESIAESSIiAR1Pg3333XVm6dKn07dtXHn30UVvdSICEafCuZiBEtrfTp0/LxYsXpV+/frZ6uEICJEACJEACJEACJEACJEACJEACJEACJEACJEACZhBw2wCKDIoDBgxQbb/66qsqo6JWJHPmzJInTx5ZtGiR3sQlCZAACZAACZAACZAACZAACZAACZAACZAACZAACQSMgNsGUMSly5QpkyBuZ9myZeMp/vzzzysDKOIowRiKuAgJCTxAT506JefOnUuoCLeTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgMcE3DaAIinHgQMHZPPmzVKzZs14DTdu3Fhy5swpGzdudDmY8dChQ+XKlSvx6uIGEvCGwNWrVyU2NlaioqKY0MAbkDzW7wQQE/ngwYOSI0cOJkLyO3026C2Bu3fvelsFjyeBgBC4du2aHD16VI0bmNAgIKeAjXpIQOdSuH37toc18DASCBwBOETdvHnTZdtB4DRlyyRgT4DjBnseVvjmURKkrFmzSp06dZwalTJmzCht27Z16wbWrl07K7CijhYjsGfPHtm+fbsyglpMdaob5gQQG3nbtm3y119/hTkJdt+KBHQccL20Yh+oc3gS2Lt3r+zYsUOOHTsWngDYa8sSQD4FCB1KLHsKw1pxOE6tX79eMDuUQgJWIrB//341bvjnn3+spHZY6+qRATQpYl27dk2qiN3+7NmzS9OmTe228Ys9AXgzUtwjoL2Q9NK9o1maBAJHQF+zehk4TdgyCXhOgNev5+x4ZGAI6GtWLwOjBVslAfcJaA9QvXS/Bh5BAoEjoO+5ehk4TdgyCbhHQF+zeune0SwdCAI+MYDmy5fP7b6UKlXK7WPC6QB4MiK8wMiRIxkzNZxOPPtKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgFQG3Y4Am1Rrcfz0xgCZVb6jsh2v/n3/+KVu2bFGJpEqWLCnlypWTyMjIRLv40EMPyfjx46VixYryxhtvSLNmzeTbb79N9Jhw35k2bVqFQC/DnQf7bx0CSCIH4bVrnXNGTf8jkCxZMvUlefLk/23kGglYgIC+5+qlBVSmimFI4MyZM7J27VoxehzhuQJy6dIlmTt3rh0VxBPHcwSFBIKVAO658F5m7OVgPUPUKyECeryglwmV4/bgIWCqARTxvmrXri0//PCDVKhQIXh6GSSaIL4J4p1u3bo1nkbNmzeXQYMGSaFCheLt0xuyZcsmTz/9tAwbNkwmTZpEA6gGk8CyWLFiKpFB6tSpEyjBzSQQnAQQS7lBgwaSMmXK4FSQWpFAIgS04fO++3wyySSRlrmLBLwjUKRIESlQoIBw3OAdRx7tWwJt2rSJZ+TULe7bt09iYmL0V9ty06ZNUrZsWdt3rpBAMBGIjo5WBlA9fggm3agLCSRGoHDhwsr5j+OGxCgF1z5TDaDoGv7xwkuxWrVq0rlzZ2WwS5UqVXD1OgDaYOBRuXLlBIM7T506VWbMmCEDBgxQHp4JqRgREZHQLm53IAAvJN6MHKDwq2UI8L5pmVNFRR0IaA9Qh838SgJBT4DjhqA/RVQwjgAMoLhWjfE+4YSCcFl4TsiVK5cdJ+RawEM6hQSClQA9P4P1zFAvVwjQ3uAKpeApY7oBNG/evMqAd/DgQXnzzTele/fuKit8x44dw/afL6a9w/NTZ7ZLkSKFvPDCC8pQjHAByDoKA+mCBQukT58+anr8N998I84MIDiWQgIkQAIkQAIkQAIkQAIkEH4EEAYLHwoJhDOB/v37y/vvv2/3IiCcebDv4UPgyJEj4dNZH/TUVAMo3ka+88470qlTJ6UqpmrDqPf5559L8eLFpVatWmofkvmE05seHfMTUHLnzi0LFy4UY9Kn+vXrK17Xrl1T09s/+ugjqVevnvzyyy+SJUsWtY9/SIAESIAESIAESIAESIAESIAESCDcCaxatYrGz3C/CMK0/3///XeY9tycbpsaoAvxvrTxE+rBIIo4djNnzpQDBw6oKeBdunSR/PnzK0Pp4cOHzelFkNeCKSlaYAw2Gj/1diyR+AQGZHiEwpW6Ro0aEhsbayzCdRIgARIgARIgARIgARIgARIgARIIewJwLEI4CH7IINSvgVatWoX9790MAKYaQBNTCEZPxLeEy+6IESNkwoQJcv/996tA3XPmzLHLZJhYPVbcd/ToUaU2Eps88cQTSXYB8XvmzZsnjz/+uIqlun///iSPYQESIAESIAESIAESIAESIAESIAESIAESIAESIIH4BPxmAEXTMAQOHTpU3n77bbV+9+5dlcUQmc0//PDD+NqFyJaiRYuqnhQsWFB5xbrSLXjTYir8W2+9JbVr15YdO3a4chjLGAjAe3b58uVy5coVw1aukkDwE7hx44asWLFCvTAKfm2pIQnYE0AyDgoJWJHAiRMn1Ljh8uXLVlSfOocxgZs3b6pxw6FDh8KYArtuVQJ4zl27di2ntFv1BFJvErAQAVNjgOKhZ+DAgdKvXz8bAvxDnj17tnz99dcq9qXxwahQoULy4osvSvv27SVHjhy2Y0JtBdnfYdDE1Hb0P3ny5C53EcmSkCgpJiZGpk+f7vJxLCgqfMC5c+fkzJkzki5dOiIhAcsQOH/+vLpuca8oUKCAZfSmoiQAAni5CdGJ/9QX/iEBCxDAi1OMG06fPi3p06e3gMZUkQT+JXDhwgU1bkD4saioKGIhAUsRwAxR2AzwcZYE2FKdobIkQAJBTcBUAyh6+v3330vLli3VP+EffvhBJk+erAaSmgKSH2EaOLLCP/rooy57ROrjrbiEByiYTJo0SRYvXqwSHLnTDyRJ+vHHH6V58+ZSqVIldw5lWRIgARIgARLwGQE8tFy/ft2ufm0A3bdvnxoL6J0pUqQQzISgkAAJkAAJkAAJkAAJkAAJkIC/CZhuAN29e7cUKVIkXj/gxQRvT3g0IhN6uMngwYNlzZo10rZtW9m4caMgzqc78tBDDylPWhiNKSRAAiRAAiQQaAJTp06VFi1aJKgGZj84CsLgvPbaa46b+Z0ESIAESIAESIAESIAESIAEfErAdAOoo7YwdmJKEQx3zzzzTFgaP8Ekb968ygD67LPPKi/O4cOHS5MmTdTUeEdmCX1/4IEH5I8//pC6desKEyMlROm/7Xny5FHxP7Nly/bfRq6RgAUIZM6cWbJnz67uGxZQlyqGKQGEsSldunQ8D1D8z0e4F4RvMQpmgJQqVcq4ieskEFQEMG64dOmSuv8GlWJUhgSSIJApUyZ13Tred5M4jLtJICgIREZGqme2lClTBoU+VIIESCB0CfjEAAqD0yuvvCIdOnQQZH9funSpSnIEA17Tpk2lT58+UrZs2dClmkDPYNRAdvd169apUAEIDzBjxowESjvfjLg+2gjqvAS3agLwsnXX01YfyyUJBJIA4h9FR0cHUgW2TQJJEqhQoYJs2bIlyXIsQAJWIZAzZ07Bh0ICViMAwxHHDVY7a9RXEyhRooRe5ZIESIAEfErAdANo4cKFVQZN4zT3WrVqCT6bN2+WIUOGKA9IeIS++eabUr16dZ92MBgrRxxPb2J5gi0fOoPxzFInEiABEiABEiABEiABEiABEiABEiABEiCBYCNwn5kKIfMgMsAbjZ/G+uH1OWXKFEGc0FOnTkmNGjWkWrVqMnfuXGMxrrtAAFMJKSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAokTMNUAet9990mrVq0SbPHMmTMybNgweeyxx2T9+vWq3MqVK6VRo0by3nvvJXgcd/xH4NNPPxVMg+/UqdN/G7lGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTglICpboT37t2T1atXS9WqVW2NYRtigH711Vcybdo0uXHjhm0fYoW2a9dOXnrpJaeZ420FuaIInD9/XsVPvXnzpowbN04Zm+FBSyEBEiABEiABEiABEiABEiABEiABErAmgcGDB6s8Ie5ov2jRoqCOXY2ZvpgBvG3bNrtupUiRQnLkyKF0RwxYJIcuVqyYXRl8Qb4UOIDBDgLBjOMnnnhC2rZtK0jGaZRBgwbJnDlz5PLly5IlSxZ5/fXXpWHDhsYiXCcBMdUAevfuXXn77bdl1qxZcvjwYfn5559l0qRJcuDAATvUMNrBgxEJkZDsg+IagYwZM6qs0AcPHhTcNBx/9K7VEj6lYHy/fv26pEmTJnw6zZ6GDAFcu0hqAM96CglYicDt27cF4wFmc7XSWaOuIMBxA68DKxPguMHKZy+8dee44d/zD5sJwgR+8MEHgkSTWbNmlV9++UW6desm+fLlU45m+J0fP35cOZdNnDhRGfuCOXkfDJD4wMi5c+dOFf5w9uzZcuvWLWUj+uuvv5SBE7lhypcvL5999pk8/PDDth8EDKMVK1aUUqVKycWLF6V27doyYMAA237jyltvvaX+j7///vsqXwoScFNIwJGA6U/W8PZEtvMHH3xQTWvXxs9MmTJJly5dZPv27SqLecuWLWn8dDwbSXyHIWTZsmUydOhQWbt2bYKxVpOoJmx279q1SxYuXKj+kYRNp9nRkCCAf/ALFiyQrVu3hkR/2InwIrB8+XJZvHixMoKGV8/ZW6sT2LNnjxo3nDhxwupdof5hRuDSpUtq3ICEsxQSsBoBhMSDJ+OdO3esprqp+l67dk1Gjx4t7du3lzJlykj+/PmVERSNIP8HjKBIOA1nsm+++UaQXwUG0UAInN3Gjh3rctPau7N48eLKVgTvTxg6MRsY960JEyYooyVmEmOmq1HAoXv37moTyl69etW4224dnqKtW7cWGj/tsPCLgYDpBlDUDc8PLch2/vXXX8uxY8dk1KhRUrJkSb2LSw8I4Abw2muvqRueB4eH1SH4JwLRy7DqPDtraQL6mtVLS3eGyocdAVy3CNUS7g8yYXfiQ6DD+p6rlyHQJXYhTAhoIwiv3TA54SHWTRi04BEIT9BwluTJk7s8ZRtTwZ955hm78IL+YocxXvPmzd1yMkosgTOcvNq0aSP9+/dXdiQ4zeFlulFgAMWsznPnzsnkyZONu2zrsEH9+OOP0rlzZ9s2rpCAIwGfGEBxceLNxYYNG5SnItbTpk3r2Da/kwAJkAAJkAAJkAAJkAAJkAAJkAAJkEBYE8CU9tSpU7vM4I033pBy5crZyh86dMhmRN6xY4cyFsIJDdvxuXDhgioLA6behqUzj0oYE//44w9lz4FxWpdBPhcYPzEb9cqVK8oIquu1KeLhCqawR0dHqz44TnNH7hgYSSHDhw9XU90dm/n9998le/bsKnyA4z5+JwFNwNQYoKi0aNGiapp2RESEboNLixPYuHGjSmBl9Oz1tEu4CUPOnj0riPmRPn16KVCggNPqEAMFHy25c+dWAY31d71EzC6EWtAJtvD27P7777fFTsR3LXgzjhs9joH4sn3EaXUUtk/+rlx/Ou4nBhb4nUDcvf6tfv3BC0D3PZT6v3v3bnU+4R155MgRn93/Ann+9fWrl6rDcX94/7PO/Q/nDNPb9G8wXO4/+po13nvBIlz6j75CvB1/BfL+g/FfOLavPT/hFeZMrH7/ddanhMb/4Xj+wUc//1ix//rei6U79x9n10W4bcP/q+nTp8t3330nv/32m/rf3aFDBxUSIyoqSm2HRyTCan344YcCoymemREmDrlbTp8+LT/99JPKzaLZjRkzRhk+4WGKuJ0xMTHStWtXgYESRtp9+/apojNnzlThDTGVvW/fvvpwj5c4/48//rggJAJCKcXGxtqF/OvZs6eaHg+dfv3113jesphG/+KLL3rcPg8MDwKmGkBx0cLtmMbP0Lp43n33XUEGNzPl77//lr1796oq8WDh7J81btTI4qYFb5eqVKmiv9qWiHuE2LJGwdszGOOROMp4PeKBDjG+jOKr9iMjI43NqHW2T/6uXH94e4kEcRjU6N+Ju9e/1a8/DM503/HjCZX+60EjXiht2rRJDex8cf8L5PlHgj68qTe+fMI55P3POvc/nK+jR4/afoOh8vtL6v5bpEgRSZcunSDZJO6/WsKl/7q/3o6/Ann/wfgvnNvHvdeZWP3+i3GRoyQ0/g/n82/V6x8JfzDuw3jI1fsPw+z8+4vAszK4IQ8Lxpb9+vVTRkAkUAJTxNRE7hVw1ZIhQwZ56aWXZNWqVcqgqbdjif+TSLqEpU64jLr08TgOHqQwiCJ+JzKtmynIIwPBCw6MmfGcrgXP9o0aNVIJt4cNG2ZnAEX+hPnz5wuMtxQSSIyAqQZQvHVEMF6KawTgSo63F/hx//PPP+phAz9eDF7wjx5vbWrVqqXcuB0fJF1rwZxSSLpUvXp1m9ekN7WOHDlSvc3B4ARBkOGB6ezhH23ApR9vpSC4tnLlyqXWHf/AyImyOv4RWOXNm1cFi4YngFEKFiyotmtvVl+2b2xXr7N98kcMnKSuP1zvDz30kFfXv77mjEsrXX8YxOMeAfHk92/st14Phv4b71OIke2r+5/us3Hpr/7j/u4s1pO/2sf935mwfdfvv+CH2Rn4DYbS7y+p+y9COOGBL0uWLGF9//V2/MXfX3wC/rr/ODP+QRt/te+r+6+zxGQJjf/j07d+/531KZT6bzRwu3r/cTbOcMYp1Lfh+RiemgMHDhRMe69Xr548/fTT6qP7jmTUzgT/6xxl//796jllzZo1NgNo48aNVT4Xx7IYH5gteuyPep397l999VVlAF2yZIlyJMD1AoETHjxVE+qrKsQ/JAACcdZ1ip8JxLl034vLTnYvzviGedhJfuL+wd2Li6N6L27qpJ81Nb+5uJuU6u+TTz5pfuWskQRIwPIE4qbcqntE3ADI8n1x1oG4l1yqf3Fv353t5jYLEChRooQ6h3EPGhbQ1n0V4x6eVP8WLFjg/sE8wrIEPvjgA3Xe33zzTcv2gYqHLoE4bzR1fcZ5aYduJ9kztwg8+uij6pqIm8rt1nFWKxyX8Ef1M84xKlHV69Spo8rFhZuLVy4uW7vaFzcF3m5fjx491Pa4KfC27XEOWvdge4gzMN9777337uG7o8QltlbHDRkyxHFXgt+bNm2qjombop5gGeyIi+OpysFGsmXLFqdlK1asqMq0atXKtj9ului9FStW2L6H4gr6Cy6dOnUKxe75rU8+SYIUd2IoTgicPHlSvY155JFHZNKkSXbTu50Ut22CV+j48eOVJ8bLL79sC25sK8AVEiABEiABEiABEiABEiABEiABEiABEvCQAGZCfPHFFyoZE6bTIzQMYos6E194gCKJNgThaEqWLOmsWenVq5faDq9PhOtBfP3z58+rBEpOD+BGEjAQMHUKvKFerjoQwFTuGjVqqB8odmFqGWJc4KaSP39+SZs2rfpg2icClSOuDT4wfuJHjUxr+IF//vnnAtf02bNnS8qUKR1a4VcSIAESIAESIAESIAESIAESIAESIAEScJ9AixYtpHz58vL888/Ln3/+KQ0aNJBPPvlEunfvbleZLw2gCAWWUAjAZs2aqWROyCmC8HrIQ4PETxQScIUADaCuUDKhzAsvvKBifXbp0kUFJi5durTbtSKDOrK3Ibjv6NGjbW8/3K6IB5AACZAACZAACZAACZAACZAACZAACZDA/wgg+RFi+cJJC0mSvvzyS4H9Ahng40LyCRIoaTHbAIq4njNmzFDen8OHD9fNxFsi/iuMsa+99pryVs2cObOsW7cuXjluIAFnBDgF3hkVk7ft3LlT4mJpCX7UcTEzxBPjJ1TKkyePessxb948FehYJ1IxWd2QqQ5JpuAtyyyBIXNKw6YjcUFQ5MCBA8oLPGw6zY6GDAGEe8ELOwoJWI0AZuAgMeXt27etpjr1DXMCGDccPHhQzRwLcxTsvgUJnDp1Ss10tKDqPldZ/z/y5nkW08khSLpslLNnz6qvyOquZdOmTTJz5kz1FQZOZH2PiwUqeK7etm2b2o4ZqxDMVHVVkrJbxMX7lLgcISpz/YQJE5K0l8TFElXGWEx9h7eoMZGWqzqxXHgSoAHUD+cdWdQ6duyoMqmb0Ryyu+FHjoEOJWECeIO1fft2lXU+4VLcQwLBRwAhMzDIiEsIFHzKUSMSSIIA4jfhTbw3g/UkmuBuEvAJgb1796osujTg+wQvK/UhARgytm7dqq5fHzbDqknAJwQ2btwo69evl1u3bvmkfitXeuTIEaU+jMQ3btxIsCsIlQfBs6+jlClTRm364YcfZP78+bJ69Wrp2rWrLcs6thuPw0xTY1uYYp4zZ06bUbJQoUKqvmnTpqmXht99952cO3fOsVm779r4avz/inEiHMV69+4ttWrVkhQpUiiP07iESXbHOvsSl6jJNu0dxlAKCbhKgAZQV0l5UQ7eMAkF8fW02ocffjjeWxxP6wrV4/SbJr0M1X6yX6FHQF+zehl6PWSPQpmAvm71MpT7yr6FFgF9zeplaPWOvQllAvqa1ctQ7iv7FnoE9HWrl6HXQ/d7hBfJmOY9bNgwdfD169elfv36MmjQIDtDMZwlYABEzhDIq6++KoMHD1br+g/yjqAexNR87LHHJC77uzRs2FAef/xxeeCBB1S8TyN7vFCBwxVyj/Tv318w+3TWrFmSPn16VWXVqlWlUqVKymiKZbZs2SRLliy6Obvl3Llz5ZlnnlH5TLAD3yMiIqRgwYLKPgJ94eWKMH+YuelOLE/wwVR9JJimkICrBAISAxQ/6GLFigks9+Eg+fLlk2XLlpnWVUxz2bx5s+i3OaZVzIpIgARIgARIgARIgARIgARIgARIgAQCRgCGRXxGjBiRqA4lSpRQXpOI1ZmYIHN6t27dBI5ZCKsHqV69utpmPK5JkyaCJEjwAMVsU8T8hBHUKKlSpRLMcEW4rsjISEFMzoQEhlZ84GVqtqDtRYsWidmxSM3Wk/UFF4GEr1Yv9bxw4YKMHTtWTcdAIN0hQ4bYakSMpXLlyknnzp1V8FrbjhBdgbdm27Zt1RsXxLbwVhCHAy7iCPhLSZhA2rRp1U69TLgk95BAcBFIkyaNUojXbnCdF3e1QUyl5cuX28UTRAwlCN52YxqSURBTqUaNGiqbpXG71dZx3cJTIaHsnVbrD/UNHwL6nquX4dNz9tTqBDBugBGA167Vz2R46o/rFg4+iRnSwpOMub0GX238RM06NqixlZQpU6qvMHLCOzQhwf1GT4VPqIw/thcoUMAfzbCNECLgEwPo4sWLlcFPx3po3LixHTI84MGVGm8DEGh3/Pjxgh9ZqApuDk888YTgjUqzZs1UPNCaNWu6dZNHjIzff/9dPv74Y1m4cKFySQ9VXmb1C17GUVFRogM1m1Uv6yEBXxOAd3yDBg1ED0J83R7r9w0BTEFyfGuuW8KLQExDchS8wXdn+o/j8cHwHR4FeJBBzCgKCViJAKbS4WGK4wYrnTXqCgKYmorpsXCQoJCA1QhER0ercQNfnFrtzFFfErAeAdMNoAjUCy/Hy5cvJ0qjaNGialp44cKFJVeuXPLJJ58kWt7qO+ENi+DCP/30k/pkypRJxb1A/2GkwxsYvP3CG1x4Bl25ckVlW0NAYwQHRkIUnant7bfflk6dOlkdic/1x5spPsT4HDMb8BGBUH4p5CNkQVctYishIZBjUH8kaMNDKuIfGQX3K7wcs7rQg8PqZzB89ee4IXzPfSj0nOOGUDiL4dkHjhvC87yz1yQQCAKmG0ARXwLGTxjzqlWrpqZpG7OIGTsJF+yWLVuq2BYIjvvQQw8Zd4fUOrK2L1myRHl/zp49WxAiYNWqVerjakdhHAXfgQMHunoIy5EACZAACQSIAGI3IWg8hQRIgARIgARIgARIgARIgARIILAETJ+fhkC05cuXVxnKFyxYIM8991yiPSxVqpQg69iUKVMSLRcKO3Pnzq0ehjH9H1wQVNgVyZEjh5pGefjwYRo/XQHGMiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTwPwKmeoAeO3ZMeTaOHj1asmTJ4hLkc+fOqXJbt251qXwoFEJsP3yQJAIG471790psbKwcP35cLl68KDCUIgYVPvnz51cZ4HRSlFDoP/tAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAv4iYKoBNGfOnCqGZenSpV3WH9PAIefPn3f5mFApiHhvMTExTruD7MEnT55UyaFo/HSKiBtJgARIgARIgARIgARIgARIgARIgARIgARIIEkCpk6BRwDjcuXKqWQ/SbYcV+CXX36R3377TRWtWLGiK4eETZlNmzYp789GjRqFTZ/N7ii8apcvX64SSpldN+sjAV8SQNzkFStWCJLKUUjAagSQtG/9+vVWU5v6koCcOHFCjRuSSuRJVCQQbATgOIFxw6FDh4JNNepDAkkS2LFjh6xdu1Zlgk+yMAuQAAmQgBcETDWAQo+mTZuqeJWY3p2YzJw5U9q2bWsrUrlyZds6V0jADAIwgCLEwpkzZ8yojnWQgN8IwCMe1+3Ro0f91iYbIgGzCMBwj2v31q1bZlXJekjALwT0uOH06dN+aY+NkIBZBJBcleMGs2iyHn8TwLgB918Y8ikkQAIk4EsCpk6Bh6I9evSQP/74Q2rUqCGdOnWSZMmSKf0vXboku3fvlp07d8rYsWPtsp/XrVtXnn/+eV/2k3WTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmEIQHTDaAweE6cOFGaNWsmL7zwgkKKqfEZM2Z0ihcZ43/++WdBGQoJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJmEnA9CnwUC5Dhgwyf/58mTdvnpQpU0Zu374dT+dixYrJ5MmTZd26dZIpU6Z4+7mBBLwlkCdPHsmaNatky5bN26p4PAn4lUDmzJkle/bskjdvXr+2y8ZIwAwCkZGRki9fPkmRIoUZ1bEOEvAbAT1uwP2XQgJWIoBnKVy3uPdSSMBqBDBuwP03ZcqUVlOd+pIACViMgE/dLhs0aCD4YPr7rl27VGDu3LlzS9GiRQUZ4ykk4EsCERERgg+FBKxGIFWqVBIdHW01takvCSgCpUqVIgkSsCQBjE05PrXkqQt7pWE44rgh7C8DywIoUaKEZXWn4iRAAtYiYLoHKLIPIoOxUeARWqlSJTUtvlq1anaDy27dusnFixeNxblOAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqYQMN0AWqVKFdm0aZPLyrVu3VqaNGnCrG8OxBAztVatWlKhQgWHPfxKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgKgHTDaCuNqzLwcC3cuVKlRleb+NSpHjx4rJkyRIZM2YMcZAACZAACZAACZAACZAACZAACZAACZAACZAACXhIIKAG0HPnzsmgQYPUlPnFixd72AUeRgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALOCXidBKlFixYybdo0W+137tyROnXqyP/9379VJ0uWTJx9rl+/LleuXLEdx0zwNhRcMYnAvXv3BNdZmjRpTKqR1ZCA/wjg2kVSg/vuC+h7Kv91mC2FDIHbt2/L3bt3mc01ZM5o+HSE44bwOdeh2FOOG0LxrIZHnzhuCI/zzF6SQDAQ8PrJ+scff5Rhw4aph3QYPyHXrl1Tmd+R/R0Jji5cuCDnz58XeHyePXtWzpw5Y2f8TJcunbRv3z4YeFCHECKwa9cuWbhwoZw6dSqEesWuhAMB3DcXLFggW7duDYfuso8hRmD58uWCWR0wglJIwEoE9uzZo8YNJ06csJLa1JUE1HMXxg2bN28mDRKwHAGEw1u0aJFoW4LlOhCECh84cCDgWuEZ/K+//gq4HlSABIwEvPYARWXdu3eXihUrSkxMjDJ0litXTpDEJzGBV1P+/PmlSJEi8vTTT6uYl4mV5z4ScJcADPEQvXT3eJYngUAR0NesXgZKD7ZLAp4QwHULbw48yNCD2ROCPCZQBPQ9Vy8DpQfbJQF3CcD7E8Jr111yLB8MBK5evSq3bt1SY4fkyZMHg0qm6HD06FEZMWKEeimM/iUltWvXVuWTKpfQ/r1798qXX34p8+fPF6wH4n5w48YNGT16tJohvGbNGunSpYtXfUqor9xOAp4SMMUAisajo6Nlzpw5Uq9ePfnss8+kcuXKnurE40iABEiABEiABEiABEiABEiABEiABEjAkgTy5s0rQ4YMkY8//lh69+6t+rBv3z7Bdi2XL1+WQ4cOSd++fQWzELyR+++/X3r06CHDhw+XVKlSeVOVx8cifFfPnj3VDMzVq1d7XA8PJAFfETDNAAoFYQSdOXOm5M6d22V9jx8/LtmyZZMUKVK4fAwLkoArBLTnUSi9SXSl3yxjfQL62tVL6/eIPQgnAvq61ctw6jv7am0C+prVS2v3htqHEwE91uW1G05nPXT6qq9bvfR3z/bv3y9Tp06VY8eOCYyIzz77rERERJimRvHixW11oX7kR9GSOnVqyZ49u3z99dfy/PPP680eLXEfyJMnj+TKlUtgWA2E6NwvVapUCUTzbJMEkiRgqgEUrdWtWzfJRo0FEPPj+++/VzedQN30jPpwPXQIFC1aVIViMPMfWOjQYU+CmQAGQqVLl1YDomDWk7qRgDMCCImDKfD6gdxZGW4jgWAkgLBMiEtv9M4JRj2pEwk4EsiSJYuUKVNGsmbN6riL30kg6AlUqFBBMHU6EA5Ro0aNUh6Lxvij77zzjkyePFmaNGliCjudHDqxymC4fPPNNxMr4ta+QNtVAt2+W7BYOKwIeJ0EyVta6dOnVzEifvvtN2+r4vEkYEcgbdq06i0eH8LtsPCLBQjg7WnBggUlQ4YMFtCWKpKAPYEcOXK4NRPE/mh+I4HAEUiTJo0UKlSIxvvAnQK27CEBjBuioqKSzMHgYfU8jAR8SgAv/gPx4gmJl7p16xYv+RJikj7zzDOChLr+EEyBR8KgRx55JF5z2IfkZjdv3oy3T2+At+eSJUtkx44dcu/ePbUZ94SEJKk68RL74MGD6vArV67Ipk2b7KpCe0h4OW3aNJXkiEkv7fDwS5AT8IkBNDY2VsW7aNGihfohV6tWTZx9kCwJCZAgs2bNCnJUVI8ESIAESIAESIAESIAESIAESIAESMDqBBCbMyGBwRHeof6QcePGqaRFxrYWLFgg9evXl8GDB0vbtm0lZ86c8tVXXxmLCAyP8Bp94oknlMHy22+/lYYNGwqMls4kqTq3bt0qr7/+ukRGRsonn3yiQhvCM7V8+fIyceJEVeWMGTPkwQcflCNHjiijLMIFYAZwoKbcO+snt5FAYgRMnwL/999/S/Xq1eXw4cOJtRtvX82aNeNt4wYSIAESIAESIAESIAESIAESIAESIAESMJPAtm3bEq0uqf2JHpzATmRG19PDMe0fXqaff/65NGrUyHbE9OnTVVLpX3/9VZBUCBnkYV958cUXVWzSmJgYVfall16SLVu2yIoVK2xJj1atWqXyssCr1iiu1Il2/vrrLxULdcOGDSokDIzESHANoyhyt8Dg+dRTT0mrVq1U9YhpiuTXMJC+8sorxia5TgJBScB0Ayg8OmH8xBQ4xKFBzIsDBw6oqchGAnDP3rt3r5QoUULw49WeoMYyXCcBEiABEiABEiABEiABEiABEiABEiABMwlkypRJGfsSqjNjxowJ7fJ4OwyKeno6DKC7d++WCxcu2Oq7fv269OrVS3mfwvgJQWzUpk2bCoynH374ocAAinUkTho0aJDN+ImyVatWjRdCy9U6EYsV3qZz586V8+fPK+9T6ArDK+Sff/5RBlmEitECwygE0/QpJGAFAqYaQOFuvWfPHlm6dKkYPToRQ+P9998XJKUxCrxF27RpI61bt2a8JSMYrpMACZAACZAACZAACZAACZAACZAACfiEALwud+7cmWDdmFputvz00082AyjqhlNYgwYNbM0gLwqcyUaPHi1ffPGFbTvikpYsWdL2fcSIEWq9Tp06tm16BYZdGFe1uFonyiODPAQep9pQqzbE/cmXL5+cPHnSZnA9d+6cYNo9BHFFKSRgBQKmGkDhgv3kk0/aGT8BAW8N8CMdM2aMHZP8+fNLlSpVpGPHjjJlyhS7ffxCAt4SwD8KxKNFUHgmQvKWJo/3JwEMhhB8HJ70TITkT/JsywwCGBwjgD7iRlFIwEoErl27JkePHlXjBley9lqpb9Q1tAlg3AADRLZs2ZgIKbRPdUj2DgmAEHPT34mQ+vTpI5gavm/fvnhco6OjpV27dvG2m71Be1gieS9EG2QRf9TReczYNhIeYSo9vDadidF46Wqdzupx3JY6dWrF69NPP1XPKE2aNFFFcA+ikIAVCJiaBAmBePVbA2Pn8WZi5cqVcubMGeNmtV6vXj35/vvvZf369fH2cQMJeEMA3sjbt29XRlBv6uGxJOBvAqdPnxbEHUIcHgoJWI0A4katW7cuXlZVq/WD+oYfAYRmwkPlsWPHwq/z7LGlCZw9e1aQwATXL4UErEZg48aNyhaAGJT+lCxZsigbRcuWLW1ejenTp5euXbsKEgZh6rk/BNPby5Ytq5rSCYySykCPl3WwvWCqelLiap1J1YP9iAeKmb4wDmM6vnE6vCvHswwJBJqAqQbQggULqunvjp3CGwjEk8CPG293jAIjFWTy5MnGzVwnAa8J4J8CRC+9rpAVkICfCOhrVi/91CybIQFTCOjrVi9NqZSVkIAfCOhrVi/90CSbIAFTCOhrVi9NqZSVkICfCOjrVi/91KxqBtnVYYe4ePGicprBtO6RI0eqBED+1EO3VaRIEbU6depUvcluuX//fuUliuRDkNWrV9vt11+MHqCu1qmPTWiJqfRIdNSjRw+pWLFiQsW4nQSCmoCpBlBMd0MQ39q1a8sHH3ygpr1fvnxZAcA0dwT5RbIjZCeDIXT58uUyYMAAtZ9xI4L6OqFyJEACJEACJEACJEACJEACJEACJBByBJBwKCIiQiVwNrtzCAukJSkj76OPPiqYDo/wgDA4GgXenkh6VKxYMVvc0OHDh6s4orrcnTt3BOFcjDFAXa1T15HQcubMmWqXcbo7crpAkuqXKsQ/JBAEBEyNAYo3DYMHD5annnrK5gmKDPCI/5kmTRr1g4W79Jw5c1TMCuMPBT9kCgmYSUDHUtFLM+tmXSTgSwK4X0J47fqSMuv2FQFct8g4ytjLviLMen1FQN9z9dJX7bBeEjCbAMYNeA7jtWs2WdbnDwK4bmFUC9XYy8iergUGw6i4/BQJCcIJ9uzZUzmTPfbYY9KhQwepXLmyILzQvHnzbHaU3r17y/jx4+X333+X9u3by1tvvaVicg4bNkx5syKcwMCBA9UM3AceeMClOqETptZDEEbOUUqXLq02wUMW63B8+/XXX9V5g6MbQhriXoRkTchlANH1qS/8QwJBQMBUAyj6g0C4SHaEHy5+eNWqVbN1E9PgEQf0tddes3tLgEQfvXr1spXjCgmYQQBGdfyDQbBmCglYiUDGjBnVm128jaaQgNUIIHMoHmQQnJ9CAlYigGmCBQoUCPlxwy+//KLi9BrPDWL1Q5YuXSpvv/22cZeUKVNGmjdvbreNX4KLAOIW1q9f328xC4Or99TG6gSQcAjjhlB7cQrjHzw0daZ0nCfYSjAj9s0330ywv3qG7NChQ2XcuHHqg/9PMHjCmAlBktQ///xTnn32WZk4caJMmDBBxeMcMmSI/Pzzz6ocwhMiczvElTph2Pz8889VeUyt79SpkzLA6unuzz33nMyePVsZPWGYbdWqldINsVK/+eYb9b8Dff3tbNt0AABAAElEQVToo49s9SxcuFD69+8vL7/8siDcAIUEAk0gWdzNxicpuxBHAx/9ozN2FEGFZ82aJZs3b1ZvCPr16+f3rG9GfbjuPwLly5eXTZs2yZNPPikzZszwX8NsiQRIwBIEkKmyRIkSUrx4cSaBssQZCz8l4dmABGVINoJrNdQERhQ8sGCshkSVlNAjgAdnHaLKld7BK4te3a6QYhlfEUCCMmTEhhFI54/wVVus1xoE8P8JU8Tx/wpTvENREDIQnphZs2aVyMhI5eXtrJ+YGg+7C17gQRDHFAmenImrdTo7Vm9D0jXUb4wzikRL6dKl00W49AGB1q1bq3i1MExrQ7UPmgn5Kk33ANXE4MGEjzPB4BofCgmQAAmQAAmQAAkEKwFM4cJDhVFgCIIgQ6sxYy08V+CZEapT+IwMuG5tAvAOwlRFo+C6hmMCpjU6TqPGtlDzzDL2neskQAIkEIwEMBMMzkNJSebMmQUfLQkZP7Hf1Tp1Xc6WMMg6Co2fjkT4PVgJ+MwA6k6HEaj3u+++Uy7W7hzHsiRAAiRAAiRAAiTgCwLLli2TWrVqJVg1prA5SpcuXWTUqFGOm/mdBIKKAB0Rgup0UBkSIAESIAESIAE/EQgKAyiC9xqDA/up72yGBEiABEiABEiABJwSQOwsxDNFkH+jnDp1SjDVC1PNjHFO4SGHOGYUEiABEiABEiABEiABEiCB4CPgEwMowopu3LhR1qxZI4gRcenSJblz547T3u/bt0/FmXrjjTec7udGEiABEiABEiABEvA3ARg4ly9f7u9m2R4JkAAJkAAJkAAJkAAJkIAPCJhuAEWsLGQImzt3rg/UZZUk4DqB2NhYQdD0ChUqMCiz69hYMggI3LhxQ2XohQFGBzQPArWoAgm4RGDbtm2Ca1hnDXXpIBYigSAgcOLECdm9e7eKuYas2hQSsAoBJDZZu3atSj4bFRVlFbWpJwkoAkgqiJkVlSpVskusQzwkQAIkYDaB+8yusEOHDjR+mg2V9XlEAAZQZME7c+aMR8fzIBIIFAFkc8R1e/To0UCpwHZJwGMCR44cUdeuMUGQx5XxQBLwIwE9bjh9+rQfW2VTJOA9AYTq4LjBe46sITAEMG7A/ReGfAoJkAAJ+JKAqQZQTH1HPE9Iy5YtBQkEjh07prKk4kHI2Qdv27t37+7LPrJuEiABEiABEiABEiABEiABEiABEiABEiABEiCBMCVg6hR4xPvE28cyZcrIpEmTXHJhz5kzp/Tr109GjhwZpqeA3SYBEiABEiABEiABEiABEiABEiABEiABEiABEvAVAVMNoNmyZZOsWbOqmIvJkiVzWecsWbIIps5TSMBMAnny5FHxZHBdUkjASgQyZ84s2bNnl7x581pJbepKAopAZGSkigGaIkUKEiEBSxHAuAGJO3H/pZCAlQhkypRJXbf58uWzktrUlQQUAYwbEAM0ZcqUJEICJEACPiVgqgEUmrZp00bmzZvnttIJZYl3uyIeQAL/IxARESH4UEjAagRSpUol0dHRVlOb+pKAIlCqVCmSIAFLEsCsJHwoJGA1AjAccdxgtbNGfTWBEiVK6NWALhGDlEbYgJ4CNk4CPidgagxQaPvGG28IMsGvWbPGZeXxxmfChAkul2dBEiABEiABEiABEiABEiABEiABEiABEvCWAOwXjz/+uPjaKevatWuCZKdGQdJeCgmQgH8ImG4AzZUrl8yZM0fatWunEiAl1g3cYDZt2iTdunWTu3fvJlaU+0iABEiABEiABEiABEiABEiABEiABEjAVALvv/++LF68WMaNG2dqvbqyFStWSLly5VS4wOrVqwtmy7z88svSp08fee+993QxLkmABHxMwHQDKPRFHFB8ChYsKOnTp0/wkzZtWilfvryMHz/ex91k9SRAAiRAAiRAAiRAAiRAAiRAAiRAAiTwH4E9e/bYEjK/++67YrZH5oYNG6R27dpSqFAh5SC2bds2WblypaRLl04++uijJJ3G/tOUayRAAt4SMN0AumDBAilSpIj6USOOBqa3J/TBfgoJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ+JtAr1695NatW6rZM2fOSP/+/U1VoW/fvnL79m3p16+fIPkzBInLhg4dKq+//rpTA+jhw4dl7NixHuvh7fEeN8wDSSDICZieBKlz5862G0jJkiWlWLFikiFDBkmdOnU8FDCMxsbGyvLly+Pt4wYS8JbAvXv35Pr165ImTRpvq+LxJOB3Arh2EYj9vvtMf0/l976wwfAigEE+wtowkUB4nfdQ6C3HDaFwFsO3Dxw3hO+5t3rPAzlugPPW3Llz7RB+9tln0qlTJylevLjddk+/IOQf5OTJk/GqgFG0SpUqdtvhJNa8eXOJiYmx2+7qF2+Pd7UdliMBKxIw1QCKgL4HDx5Uxs7vv/9ennzySZeYzJgxQ7Zs2eJSWRYiAVcJ7Nq1SzCloWrVqpIjRw5XD2M5Egg4AQRiX7JkiURGRkrZsmUDrg8VIAF3COCl5o0bN6R+/fo04LsDjmUDTgBjBowdKleuLIhpTyEBqxC4dOmS/P7775I/f34VXswqelNPEgABTAeHYxTGDcmTJ/cbFBhee/bsGa89vX3+/Pnx9nmyAaEBjx8/Lphej3F9tmzZbNUgJGCXLl1s3zF+atGihaxdu1ZNmz916pR6oQyPUS27d+9W/6sQalDHFdX7XDkeZQ8dOqSSMZUoUYIvrDU8LsOCgKmuRRkzZlTu3Ihx4arxE5QbNmxodyMIC/LspM8JIMseRC993iAbIAGTCOhrVi9NqpbVkIBfCOC6hfeBrzOp+qUzbCSsCOh7rl6GVefZWUsTgPcnhNeupU9j2Cp/9epVNYMUhkd/ypgxY2Tnzp1Om4RnKBI7myEdO3ZU1axatUoqVqwoa9assav2xRdftH2fOHGi7Nu3T32fOXOmSiw9cuRI9R1G4saNG8vbb7+tcqz88ssvKucKnMm0JHY8yqBfMDQPHjxY2rZtKzlz5pSvvvpKH84lCYQ8AVM9QDFVE952mPLujmCaXNeuXd05hGVJgARIgARIgARIgARIgARIgARIgARIwC0Cp0+fTjLWJ2KD1qtXz2sPSXh4HjhwQEaMGKE8L2Eveemll+TDDz9UzmNGxbEdL5FhG2nXrp2KEar3v/XWWzJr1iyBB2jRokXlkUceUcZSHNOkSRNVLLHjp0+fLpje/+uvv6o+Ie4pMtLDABsREeHxlHutH5ckYAUCpnqAosOIl7Fo0SIV/8sdAAsXLnSnOMuSQJIEdOxEf06lSFIpFiABFwjoa1cvXTiERUggaAjo61Yvg0YxKkICSRDQ16xeJlGcu0kgaAjosS6v3aA5JVTEDQL6utVLNw71uCgSEyF8X2Kyd+9eW3b4xMoltQ/9Gj58uPz000/K0Ig46Uhw9OCDD6qQVwkdnyxZMrtdqAeOZsbQbgiXBWPu0aNH7crii/F4eInDoNu9e3ebQTdFihTStGlTdRyMsRQSCAcCphtAn3jiCXn88cdVjAtXAeJHu3r1aleLsxwJuEQAb8bwjwVvtCgkYCUC2bNnl9KlSwvi8lBIwGoEML3roYce8mscL6sxor7BSaBIkSJSqlQpyZs3b3AqSK1IIAECyCxdpkwZQQJaCglYjUCFChXU1HAY5Pwh27Ztky+//NKlpgYMGOA0eZFLBzsUgrERcabhpQnj5N9//y2PPvqoIHeKK/Lpp5/KiRMnbJnkV6xYIRs2bFCHIqZnYvLbb78JMsOPHj1aGjVqZPvMmzeP943EwHFfyBEwdQo83LWRuOPpp59WrtTIpolg3IkJkiYh85p++5BY2XDbhzgf586dEwRHRvBkinsEwO3+++937yCWJoEgIIBBUcGCBYNAE6pAAu4TMHomuH80jyCBwBFIkyaNFCpUKHAKsGUS8JAAxg1RUVEeHs3DSCCwBPDi358CL0hX45QjMSlibrpqME2qH0hmNG7cOGnWrJk899xzgiRHmCKP7//3f/amGfyuHQVG4vHjx8uyZcukQYMGKunZ+vXrBXYXRzEer2Odjho1Sk2fdyzL7yQQLgTsf2Ve9ho/SMSQwNsMyKBBg1yukQbQ+KgmT56sQgrAoPzzzz/HL8AtJEACJEACJEACJEACJEACJEACJEACSRLAVHF4S+PjqiDEBBI1wbnGXUFowCpVqki6dOnsDq1bt66KCQoj6NmzZ5UTGbxBjWI0YGL7yZMnVUxSzLKBERR6rVy50niI3brxeDhWQeCBilmSFBIIVwKmGkDxI3v++eflgw8+CFee7DcJkAAJkAAJkAAJkAAJkAAJkAAJkECQEUCIE0wl95fA8xJT1fv37x+vSTg5YdYMvEARbzQpA+hTTz2lprGvXbvWpTBDRgMoQrxApk6dKghZ6Cj79+9XyZeKFy/uuIvfSSCkCJhqAAWZ1q1bKwPoK6+8otyyM2fOnCgwvMlANjIKCZAACZAACZAACZAACZAACZAACZAACYQCAYRVefPNN1Xczzx58th1CV6l2jMTXp1aUqdOrVYx/V4LjKTw9syWLZvd9H098xaJlbQ4Ox7GVXiwTpkyRdq0aWNnbEUyKMzcNWuav9aDSxIIRgKmG0CLFSsmNWrUUJnOHONYJASgWrVq8vnnnye02/LbcTMZNmyY2/3QmekWLlwoDzzwQLzj4cJOIQESIAESIAESIAESIAESIAESIAESCC4ChQsXVkZO2EemT5+ukpxqDZGQCEbQtm3bqiRQeruORT1t2jRp1aqVrFmzRurXry/58uWTf/75R9544w0VMxSJjY4cOaIOw1T727dvS506dWyxrI3HI0l1z549laPaY489Jh06dJDKlSurJEpIhDRnzhxBlnkKCYQ6AdMNoAA2ePBgl9yyNdycOXMKfoihKoiNunv3bo+7d+nSJa+O97hhix+IfyixsbEqKDxipFBIwCoEEMgcCeIwLSZDhgxWUZt6koAigJkdGIQ7ejoQDwkEO4Fr164J4sNFxSWTcfUlfrD3ifqFBwGMG5AFGt5hGTNmDI9Os5chQwDejUimjOnpoSZIahoREaE8LhHvE7NjS5QoIYsXL1bPqf369ZM+ffrYdbtq1apSqVIlWbdunVoiSzwSRQ0dOlR69+6tHMd27Ngh7733njRp0kTFBR07dqwtgaqz47NkySLIaA9BPUjEhA+mxiOeqDNnKzul+IUEQoSATwyg+NG5K0lli3e3vmAqj7c66dOnV67vly9fFryBcWVgfeDAAdm0aZMUKFBAqlevHkxdsoQue/bsUXFSUqVKpd6YWUJpKkkCcQROnz4t27ZtUwOmhx9+mExIwFIENmzYoB5kYmJi3HoZaqlOUtmQJIAYbHj5lDJlSjX2CslOslMhSQBJVLZu3SpwKkHCFQoJWInAxo0b5fr16+r6heNQKAn6s3z5cmVoRL/OnTsnMF4i8zuSEenp6sY+49kVXp+wBURGRtrsBs8884zy/IQ9AdnktcArFLYF7fCT0PGICTpw4EB59913Zfv27ZI1a1ZVvzFWqK6TSxIIVQI+MYC6C+vChQvyxRdfCN6AhKogyz3czJEk6vjx4/Ldd9/Z3NMT6jPeynTq1Em9+UFGeIp7BHQsFL1072iWJoHAEdDXrF4GThO2TALuE9DXLZZ6MO5+LTyCBPxPwHjt+r91tkgCnhPgtes5Ox4ZeAKhfv3qBEQgDU9MhP9LSmCU1FPhjWUxrjIaP7EPBk9HSeh4lMNLvvLlyzsewu8kEBYEAh7oAVM2YPzEMtQFcTsQnwMZ3BDo+Jtvvgn1LrN/JEACJEACJEACJEACJEACJEACJEACJEACJBBQAh55gMJteunSpdK3b994GcSio6PtMpMl1rtbt26pqZ7IcBbK3p9GBggu/Prrr0vdunUFcUDmzp2rDMBwQaeYSwCZ7iB6aW7trI0EfEcgTZo0qnJeu75jzJp9RwDXLaay0fvTd4xZs28I6HuuXvqmFdZKAuYTwLgBHl+8ds1nyxp9TwDXLZyhXAkR53tt2AIJkEAoE3DbAIr4MjqA7quvvqrizWhACOqLpAfwcqQkTgBu54h30qtXL5UNbsKECcoomvhR3OsOgWLFiklUXCIDZ7FV3KmHZUnA3wSQwKBBgwZqioq/22Z7JOAtAcSsxoMMs4l6S5LH+5sApiki7jrHDf4mz/a8JYBcA8gSHWrxE73lwuOtQQAOVBg38MWpNc4XtSQBKxNw2wCKTGaIO4G4nWXLlo3Xd8S4hAEUbyJhDE1sEAkPUGR9QzDgcBS87ULGtpkzZ0rLli2lVatWMmjQIKdxPMKRj7d9xpvwxK4/b+vn8STgSwLO4vn4sj3WTQJmEaAHh1kkWY+/CXDc4G/ibM9MAhw3mEmTdfmTAMcN/qTNtkggvAm4bQDNkCGDyki2efNmqVmzZjx6jRs3Vhnc4N2YN2/eePudbRg6dKhcuXLF2a6w2AZmiAmKbPFYTpkyRUqWLBkWfWcnSYAESIAESIAESIAESIAESIAESIAESIAESMCXBDxKgoR4lXXq1HHqpo6pmzDkuWr8ROfatWvnyz5aou7cuXPL/PnzFQtkhhs5cmRYJIayxMmhkiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAApYl4LYHqCs97dq1qyvFbGWyZ88uTZs2tX0P1xVMverRo4cyLiNB0smTJ8MVBftNAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqYQ8MgDNLGWDx06JDly5EisiN2+bt26CbLAlypVym57OH8pXbq0rF+/Xjp27Khig9aoUSOccbDvJEACJEACJEACJEACJEACJEACJEACJEACJOAxAdMNoFWqVJFNmza5rFDr1q2lSZMmcvPmTZePCYeCSN4zYMAAmTx5ssBITHGfQGxsrCxfvjys48u6T41HBAOBGzduyIoVK+TIkSPBoA51IAG3CGzbtk29xHPrIBYmgSAgcOLECTVuuHz5chBoQxVIwHUCeI7CuAGOKBQSsBqBHTt2yNq1axn+LYhO3N27d+XPP/+U27dvB5FWVIUEvCdgugHUXZUqVKggK1euVNnQ3T02HMt/+umnEhUVJZ06dQrH7rvVZxhAz507J2fOnHHrOBYmgUATOH/+vLpujx49GmhV2D4JuE0Ahntcu7du3XL7WB5AAoEkoMcNp0+fDqQabJsE3CZw4cIFjhvcpsYDgoUAxg24/4aaQxTGQq+//rrA3oEZnvjUrl1bOTldu3YtWPDb6QGjZ4cOHSQiIkLg2Hbp0iW7/fxCAlYnEFADKIxTgwYNEng7LV682Oosfa4/jCJ9+vSRw4cPy7hx49SbXp83ygZIgARIgARIgARIgARIgARIgARIgARcJoCk0EOGDJFnn31WMDsGnzfeeEP69u0radKkcbkefxZ8+OGHpX///nLnzh1/Nsu2SMBvBLxOgtSiRQuZNm2aTWH8WJAh/v/+79+qkdjH2ef69et2U5MzZcpkq4MrzglkzJhRcCM9ePCgpEiRQgoVKuS8ILeSAAmQAAmQAAmQAAmQAAmQAAmQAAnEI/DLL7/IunXr4m133FC/fn3xNh9H8eLFbdXWrFnTth6MK7Db5MuXTwoUKCBnz54NRhWpEwl4RcBrA+iPP/4oI0aMkN69e9umu7nr0p0uXTpp3769Vx0Jh4Pvu+8+WbZsmYB53bp1JXfu3OHQbY/7mCdPHmVkz5Ytm8d18EASCASBzJkzS/bs2dULj0C0zzZJwBsCkZGRamYHXtRRSMBKBDBuwHQ/3H8pJGAlAnAkwXULwwWFBKxGAOOGK1euSMqUKf2m+ty5c+Wrr75Ksr0MGTJ4bQDVjmFoDHk+rCCwO1BIIBQJeG0ABZTu3btLxYoVJSYmRjBNu1y5cgJvxcQEP6r8+fNLkSJF5Omnnxbjm5HEjgv3fWD22muvhTsGl/qP2CX4UEjAagRSpUol0dHRVlOb+pKAIlCqVCmSIAFLEsiZM6fgQyEBqxGA4YjjBqudNeqrCZQoUUKvcumEAMIG7t27VwoWLCg5cuRwUuK/TcePH5ddu3ZJ+fLlE7TH7N69W5VJnz69sttkzZr1vwq4RgIhTsAUAygY4Z/unDlzpF69evLZZ59J5cqVQxwdu0cCJEACJEACJEACJEACJEACJEACJEAC5hJAzNCOHTsqpzEYiWfMmKFm2CAXiOO0/D179sgrr7yitufKlUtefvllNZPs888/l8KFCyvF4GX73HPPqVB6nTt3FoQBeOqpp2TChAnSpEkTc5VnbSQQpARMM4CifzCCzpw5k1Ozg/RkUy0SIAESIAESIAESIAESIAESIAESIIHgJbB27VplzIRjmQ4ViARKMHzWqlVLpk+fLk8++aTqAAyljzzyiPzxxx9SrFgxtQ1enc2aNZM2bdrIypUr1ba33npLZs2aJfAALVq0qDpm37598tJLL9EAGryXAjUzmYCpBlDohtiUrsiJEyeUCzfjS7hCK7Blvv32W5V1/u7du14rghsu5O+//5bly5eLdr1HwGVHgat/bGysbXNUVJQKyGzb8L+V27dvy8aNGwWJtSDJkyeXMmXKqLr/V8S2QDDnHTt2yL1799Q2tv/v1Afy5/Vn+5H8byXQv7+bN2+qe4TWi79/3v8QkN9ReP/3zf8/cMYDlY5Vxt8ff3/8/fH+E8j7r2Pb+M77v2/u/4Ee/7navhnPpc6uq2DYhqTS8PyEJ2e7du1sKuF/8tixY6VChQrKwxNGT8QohecnElNr4ycOaNy4sTJwGsMSwe6C8sZp9Ii/On/+fDl69ChzD9hIcyWUCZhuANWw8MO9ceOGcrE2JkKYNGmS4O0FDFv4AeLHOnLkSEmTJo0+NGyWV69elV9//VXw5uWff/5RN56LFy8KkvYgkDkeOPCGBzc5GPUCJdBx1apVpjaPRFmIZ4L+li5dWozBoXVDx44dUzFl9XdcI84G4LjOjIZSlL9w4UKCBlBjRju2T/68/oLz93fr1i11j+Dv/18CvP/x/u/P/3+46pAMCP+nIbz+eP358/rj+I/jX+SU0IL7D5JDOgrH/+H9/KOdWRyvi1D4vn37dtm8ebNyLHN0UkFsT+RO2blzp3o+R9xqeH7CYGoU2F8WLVpk3CSffvqpDBo0yGZ3WbFihWzYsEGVOXToEA2gdrT4JVQJJIu7efzrCmdSD2Gsww8WAuPdxx9/rILr4vuoUaOkW7duWLWT2rVry++//263LZS/oK+ItYE4HpcvX06yq0go1bRpU2U4hru6vwWGWniCmHGp4MY7depUGThwoCD2CAY1+DgTGEDwAKYFHJwZSrEfMU0wEILAWIxsmNAXXqHG+rENxlH91tCX7StlHP6wffJ39frDtQ8vSD3wcff6d7j01FcrXH8Y0CHO0QMPPGCbsgPlw6X/+rx5e//T9RiX/jr/6dKlU/dYx2yu/mof939nwvZdu//Wr19fFi5cKD/99JPUqVNHoQyX3x+uEYwbMNbwZvzB6y8+Af7+XPv9gZyn9394RMKBwtnsOqvzP3jwoJqyi+S5iHWoxdn4X+8zLq3ef3eef4z91uvB3n9cu3g2w7jB1esfeUd+++039f/q0Ucf1V11efniiy+6lAV+8ODB0qdPH5frdVZwwYIF0qBBA7UL/dRje2dlse27776TVq1aqXidWHcU7MP2YcOGKW/O559/XuViadiwoWPReN/BGrM7ly1bpnRaunSpfPHFF8qIWq1aNVt52HQwwxKOQ1myZLFt50rgCLRu3VomT54snTp1EsR2pXhGwHQPUDz44MczZcoUu7cIW7dulVdffdWmJWJNwKV7zZo16m0EYofCVTuU5eTJk8roh5gd7gi8FMePH6+Mpni7A4/ZhP4RulOvq2XTpk0rDz/8sKvFEy2XJ08etR/XSVIZ5/DmKqkyujHUh49RkAEPg6SqVavaXP3xD8fZW2TjcXrd2/Z1PcYl2yd/V64//OaXLFkimJZStmxZ4yXkdN3Z9e+soJWuP+jqze/f6v238v0HL/nwQgqGNOODuJWuPyvzd3btY5s7/FEeRk9XfoOhdP/BmAFjByTyxNRDVySU+q/7G+7XvxX7D4M9vMDy58+vsj/rc6mX7vz+rdh/3c+Elux/cI+/EaMSxmyMG9y5/hI631bfrl8gO85w1P2KiIhQq2ClZ2pgRmlSAlsEDMcPPfSQsi3AaUjHB03qWO4ngVAhYKoBFN5K+KFu2rQp3vRjWKrxRgcCwyeyl0EwyMSPGNOsQ9kAevr0aRW0WMfAxFSiBx98UPAmE4MVGBnxQWwPTA/HQAYfGEJwDAIhIzYHrP379++X2bNnq7dkCiL/OCUAjhC9dFqIG0kgCAnoa1Yvg1BFqkQCCRLAdQsPA4TCMRpAEzyAO0ggSAjoe65eBolaVIMEkiSg4+Dz2k0SFQsEIQHMNoSdAGOHQIZ9CzQa/H579uwp3bt3V6ogbwXGUo5M8KIOUrJkSZt9ZeLEiWqmLYz9RkHZvHnzqtCDyPh++PBhZVdwrNN4DNdJIJQJmGoAxQ8M09mRWMYov/zyi6xevVptwg9w9OjRxt3SqFEj+frrr+22hdqXF154QcX67NKli8DlHnEH3RXERPrwww9lzJgximGvXr3crYLlSYAESIAESIAESIAESIAESIAESCBsCWDavKPNwhmMSpUqOdvs1jYdei2pg95//31BTE+EgcLsS8yURYIi49R2hDOAXQUJjzDrFp6zmKIOB7QPPvhA3nnnHVszsB1gevsnn3wip06dUt6eyDUCo6oWJCaGuKqjPo5LErAqAVMNoICANzhGgRfja6+9Ztv00UcfKU9H24a4lePHj0uqVKmMm0JqHTHtEPsDU1qrV6/ucd8wfRzT32NiYuSZZ56RHj160LsmEZra84hvuBKBxF1BSUBfu3oZlEpSKRJIgIC+bvUygWLcTAJBR0Bfs3oZdApSIRJIgIAe6/LaTQAQNwc1AX3d6qU/lG3evLng4w9BsmMtMErCIUwLDJpIevTVV1+p53xMSYcXJwyXMIIieXSNGjWUByeOwaxQzBD94Ycf1GxQTJcfMmSIcrDq27evIKYn7A1IboSy69evV00hpE2+fPlU4mXU2axZMxVD9ciRI2o/EibBAxexv2EM1dsxA5UxQBUi/gkRAqYaQPEmYu7cuYI3CZjWjbcLiFmJKdsQeIe2bNkyHrpp06apqeDxdoTIBry9AQdvjJ9GFIjdgSDnCAheqFAh4y6uGwggYRRimOk4KYZdXCWBoCaA3ze8xLGkkIDVCFSsWDHsp7FZ7ZxR338JICwRYnoaH07JhgSsQAAGijJlyrgUt9cK/aGO4UUACXcQOxwxLUNJYDwcMWKEYHq6lsKFC6vcFDB8IhkyPjA8QvA71nk38BwA4+Vzzz2nbCgIIQjbCpLgINa60a7QoUMH5c0K56jFixer/dHR0fLnn38qoyfqxkuSoUOHSu/evVVIPUyvf++996RJkyYqLujYsWOlYMGCsm7dOhk+fLggfB8EU/K7du0qTzzxhPrOPyRgdQKmGkDhxYkp3vfff7/ArRyZw/UbDzzIf/nll/F44UeMzGrIRhaqgoDDiNFhpuDmCLY0gCZMFTFVcS1SSMBqBPDmF4MQCglYkUCOHDmsqDZ1JgFJkyYNx1W8DixJAOOGqKgoS+pOpUkgVF/442UavDPx8URgP9iyZYuK2/nXX3+p/08wRuL37iiYHYoPHKSQcNWZ1yb2w/MTRtdMmTLZqoBNAQmWtSc5Msw7yz5vO4ArJGBhAqYaQMEBcSfwxgBvEbTgTcf3339vG1Riyju8Pn/88UeVsRDljD9CfVyoLOFuvmzZMtO6gzdGmzdvVm96TauUFZEACZAACZAACZAACZAACZAACZAACQQNgcjISMHHFUnKgQJGTke7SyiHInSFGcuEFwHTDaD4ASFTOWJLwEiHeBPwVjT+sBAXFG96XnnlFfUBcgT8DVVB/9u2bSuPPfaYPPnkk153E+7qmCKAtzsUEiABEiABEiABEnCXAKbcIWECZqkYBXHLIaNGjZKZM2fadsHj5Omnn1ZT8WwbuUICJEACJEACJEACJEACFiFgugFU9xvTMPBxJoixhE+4CKapI24GYmzA7RzxQGvWrKlczV1lgJgfiPfx8ccfy8KFC5WR2dVjWY4ESIAESIAESIAEjAQQpqh///7GTXbrc+bMsfuOL5iCh3juFBIgARIgARIgARIgARKwGgGfGUATA3Hr1q2QC3KcWH+xDyEBkOHtp59+Uh+4niOuB8IDwFCMoPuIWYn4U/DKuHLlily9elUQPBneGHhQOXv2rGrm7bfflk6dOiXVJPeTAAmQAAmQAAmQgFMC5cqVU+MRRw9QjDUOHTok5cuXtzsOHqB169a128YvJEACJEACJEACJEACJGAVAgExgGLQnTt3bpUoCcmSypYt6zSYr1UguqInpvwvWbJEeX/Onj1bLly4IKtWrVIfV45HGRhHe/XqJQMHDnT1kLAuBwNybGysMjDroM5hDYSdtwwBxPlFEHMkk8mQIYNl9KaiJAACMKjhRV6ePHkIJMgJNG3aNMg19K96165dUy+e8WIaCSEoJGAVAhg34MVFtmzZJGPGjFZRm3qSgCJw6tQpuXnzpiBpEIUESIAEfEngPk8qR0Kfr7/+Wk3DHjFihIwZM8atamAIxODyzTffVB4GiP+JbPChLjD6zpo1S+bNmyfPPfecy4YNGEEwTe3w4cM0frpxkezZs0d53cIISiEBKxFAIjl4fWO6KYUErEZgw4YNsm7dOkHoFgoJWInA3r17ZceOHXLs2DErqU1dSUDNEtu6dau6fomDBKxGYOPGjbJ+/XrBLFEKCZAACfiSgEevt0ePHi0///yz0qtatWoqKL47SsKg9+WXX0qdOnWUIRAP+/v27XOnCkuXbdCggeBz/fp1WbRokWDADSPd8ePH5eLFi8o7tkCBAoJP/vz5pVKlSsr709KdDoDyd+/eVa3qZQBUYJMk4BEBfc3qpUeV8CASCBABfd1iSe/7AJ0ENusRAeO161EFPIgEAkSA126AwLNZUwjw+jUFIyshARJwgYBHBlBd76RJk6RVq1b6q2zatElGjhxp++64Am9R47SMZ599VtasWSPYjthS4SapU6eWmJiYcOs2+0sCJEACJEACJEACJEACJEACJEACJEACJEACfiPg0RR4aFe8eHE74ye2IaHP448/rrwYJ0yYIPh89913AkNfixYtVKIflDNKy5YtjV+5/j8CiIPyzz//CGKiUDwjgKRSEL30rBYeRQL+J4B4vxBeu/5nzxa9J4DrNmXKlPT+9B4la/AzAX3P1Us/N8/mSMBjAhg3wJmE167HCHlgAAnguk2RIgVjLwfwHLBpEggXAh57gD744IPxGCFZR7NmzdTnhRdekPHjx8vw4cPl5ZdfjldWb6hQoYKkT59ef+XyfwTgTVu5cmV5+OGH5c8//yQXDwgUK1ZMxZqFAZ5CAlYiAE95hMmAEYlCAlYjUL16dUFCjvvu8/gdq9W6TH1DhECRIkVU+CGOG0LkhIZRN/AsVb9+fWVECoVuI5GenhaN/sAxBIL/LXpdbYj7g4Rl/H+jaVhzGR0drc4tw+ZY8/xRaxKwEgGPn06yZs2aaD/xTxiCh/jEBP+wkASJQgJmE8CbcD7EmE2V9fmLQKpUqcIyNIi/+LId3xHAwyg8OSgkYDUCHDdY7YxRXyMBjBtCwRD44YcfqhfA6I/+lCpVSnUVOSP0Nr2E8ReJ9yjWJcBxg3XPXbhojlm5TE4bGmfbYw/QdOnSJUpAGzX1MrHCmTNnTmw395EACZAACZAACZAACZAACZAACYQ4ATgvwLhp9ACF5ycyhMPAC2OZUfT0aeM2rpNAMBC4cOGCynWydOlSQdJnCK7fWrVqqfCAmOlJCV4CN27cECT/njZtmspb06VLF3U+E9L4119/lVmzZsnq1auVRzPKRUZGCjyc+/Tpk9Bh3O5nAvb/QUxsXHt/OP6TctYE3tzhHxuFBEiABEiABEiABEiABEiABEggPAn06NFD8KGQgD8I3LlzR+bPny/Lli2TM2fOSO7cueXRRx+VmjVret18pkyZ5N133xUkfi5atKiqr3///tKvXz+v62YFvieAUGQ9e/ZUOVlg1ExKkAsHH+S4mTJligrzuGrVKoZ7TAqcn/f7zADqTj8Q54VCAiRAAiRAAiRAAiRAAiRAAiRAAiRAAr4mgJwbrVq1ije1+YMPPlBee0jmDA8+bwXxpeEcBi/mkiVLelXd4cOHZd68edKpUyev6uHBSRNAWBx8qlSpknRhQ4kSJUqob7h2mOvGACZIVoPCAIqbAYUESIAESIAESIAESIAESIAESIAESIAEfElg8+bNgqSNV65ckcKFC0vr1q0lX758snfvXpkwYYKsXLlSqlatKmvXrpW8efN6rQpmxcLmoWfJelIhEoA1b95cYmJiPDmcx3hIwN3Yyvoc66WHzfIwHxHwOAkSYiKYJQgqSyEBswnExsbK8uXL1T82s+tmfSTgSwK4v65YsUKOHDniy2ZYNwn4hMC2bdtk/fr1PqmblZKALwmcOHFCjRsuX77sy2ZYNwmYTgCGEYwbDh06ZHrdrJAEfE1gx44dytDor5B4iC8LgyeMn88995zyAMVU9fbt28vgwYNl9+7dKk7nsWPH/OJpCcPogQMHbJh37txp9x078GwA4ycMstAb9hPEGHUU3ANg3MU9wZlg5u3BgwfVLtQDL1ijINGYjr+L/4mLFi2Ss2fPGovEW/e2zePHj8vChQsFvK9evRqvfr3h3Llzqv+J2Y5cYanrwxL/72EvQJxPJDnSfTeW4XpoEfDYA3TmzJmSJ08eQeBpZ6J/xAgcm5D1Gzc5BIvFjwbuxZT/CGTMmFHdeLUL9X97uOYqARhAcaNEPJekkna5WifLkYA/CJw/f15dt8mTJ5cCBQr4o0m2QQKmEYDhHgPsMmXKJPj/37TGWBEJmEhAjxuQrILT1kwEy6p8TgCGEIx38TwVFRXl8/bYAAmYSQDjBhjs8EECLF/L4sWLZfv27VKwYEH55ptv4o1V8Bw+depUKVSokMyZM0f279+v1s3W648//pAvv/xSZs+eLU2aNJEXX3xR2rVrpwywaKtjx44yduxY1ezEiRMFxkkI7DDQH0mU+vbtq7YtWLBAPvnkE/X7X7NmjbKvfPzxx9KhQwe1f+vWrTJ58mTBtP6nnnpK6tatK88//7xcvHhRJfqB4RF9xgts1P3yyy8L9IO9BrYc2HReeuklVZf+402b8LJFrFUkFnrkkUekWLFiah3nxtGwixfrYJE/f36BbWTGjBnKIDxu3DipUaOGUscdllp/1NOrVy8ZMGCA4JkLsVqzZcumEhlxDKAphd7SYwPo33//LW+99VaSRHr37p1kGRaIT6B48eKyZMmS+Du4hQRIgARIgARIgARIgARIgARIgARIwG0C8PiDwPsTiW6cSY4cOaRhw4byww8/KEMgjKFmC14Uw6gHxwfolDlzZpWQCQ48iE0KA1/btm2lcuXKyvgIA3HXrl2VkfT111+3qTN9+nT57LPPlGMZ+gMvSEzvh0E1IiJCTZnHNng4wstyw4YNyjkIBlIch75lz57dZkxF3e+9954yNsJg+sYbbygDJOKXIqM5xNs2ER8T7cBJ6ZVXXlF11q5dWypUqKC8MPW0c3i8wsgJPeGhC4E+2FarVi2lx5NPPqleurvKEnXA6xQGTxiDwRpy//33K9YwNmud1A7+CSkC94VUb9gZEiABEiABEiABEiABEiABEiABEiABEnBCAAZGCDK+JyaY7QpJagp4YnUktg+epjDeQdCW9uAsV66cmu6O7ZjO7ijGmbPXr19XXozdu3e3GXPhsdm0aVN12IcffqiWMCzCmAqBwRVT/WEgxRT4Bg0aSMWKFVUsVOwfNmyYMi7mzJlTZUHXTm+DBg3CbjGjTRgvEW91y5Ytqj7UCy9MeKzCWAu5c+eOMrzmypVLGX3Vxrg/qVOnVp6x8E6Fp+qlS5fEXZaYqQRjsdGwrRNeOWOu2+bS+gQ89gBF12Gxx40DF6EngrgTmG6EHxGFBMwmgH8kuMbgyk4hASsRwBtgvIk1I+i6lfpNXUODAAaQiFWVUPib0OglexGKBDBuwIMU7r8UErASgUyZMqnrFklcKCRgNQIYN+CZLSFvTLP7ow2fe/bsSbRqxAKFaENoooU93InkSBBHewoSM0HgqZiY/Pbbb4LM8Jii/sUXX9iKYkq7Y8Z5GBIh8A41GlH1QVoXeL8apXPnzsojFNPoIWa1+cQTT8g777yjsqx/9dVXyvsTU+K1YCo+jJGYru+ob/ny5QUzZhEvddWqVVK/fn3R+rvCEvfKkydP2kIuwCj+7bffqqYRnpESugQ8NoDiLcPQoUO9fsDBj/OZZ54JXcLsWcAIwOUfHwoJWI0A4h/pKSZW0536kkCpUqUIgQQsSQDeLvhQSMBqBGA44rjBameN+moC/s55AWMZDG+TJk1SS0eDH/TCdHHEuIRRDTEq/S3a4OcsMZTeB51gAISMGjVKihYtqtbN/gPDKRzftDHWrDZ1nFFwrlSpkrRp00bGjBljyzEDAygkof/LMIJCFyTRwjlNSDQvR5YwlCKu6qeffioZMmRQcVhRh2O5hOrldmsS8HgKPGI2mOHdgSRKjLFgzYuHWpMACZAACZAACZAACZAACZAACZCAVQhgujcMZvD6Q5xP5DYxCgxvjRo1UgkdkfjHmYHUWN7f69qgh3bhOQvZtWuXWvrqD2Jy6iniZrWZJUsWmTdvngwZMkR5wCIxEuKdYoYwRHsE6++OfdOOTp7apBBXtGbNmmp6PUIFGKfDO7bF76FDwCMDKN6CmOkKDut96dKlQ4cqe0ICJEACJEACJEACJEACJEACJEACJBB0BJDoBklv1q1bpzwnGzdurJyy6tWrJ2XLlpUDBw6oLOuY8RpsYjSAFilSRKmHDO7OBBnstcems/2ubENWdoSHgc0GYlabGzduVFPbkTQb4QbgxY6M7zBMQvSMInh4Ih6oo2ijr+NUf8dyzr5jGj+c8Hr06KHinzorw22hScAjA2inTp1MpYG3Kk2a/H979wJu13gmDvyLUOIS1xDSauKulCLq0paOqWvVdDrGULQ1OkW1D+WZ8S9VpXWpVlvGtKrVR02rjLorqhlKS1yCBkGDpC6hEqWCJCTs/3rXM2t3n8g5OSdn7+/sfc7ve55jX9Za3/ut39p29n73d/nHptapMgIECBAgQIAAAQIECBAgQIBAo0AM677rrrvKlcBj3vJrrrmmTLxFYix6O8Zq67fcckt9OHbjsUty/6233ioPW1Qib1H1LWoYdjW35ezZs+uH7LrrrmUbL7744nJuzvqG4k4sdhQLF2288caNT/f5/pVXXlkeU60836yYsdJ8VWIF9wkTJqSYzziuS5RNNtmkTELHXJ033nhjtWt5Gz4TJ04sz+2DH/xgl20LP1iU5dVXX12vp9q/6glcXavq+b7eVsf39lr3tX77909giRKg/QvpaAIECBAgQIAAAQIECBAgQIDAwAjEgneROJwxY0a65JJLyvknr7jiivT888+nc845J40YMaIpDZs1a1a5OGRU9swzz3SpMxYwirLwSvPVAk3RlqpUQ7Qvv/zycu7Kn//85+Uw8S996UvlvJV77rlnio5qMZQ8Ergx1D96V0ZCN0qcZ5Rqbs3ywSL+EwsSVSWOOeWUU1IshBT1RYnkcTNixjlee+21VajyNtoaCdYo0dM1FnaKxO9xxx1X9kItNxT/ufvuu1MkgmPu02qofF8sq9HHcZ0juXrppZem733ve+Wcr9EbddKkSeXcohFv+vTpZdjKr3zQw3+qRGoM3ZcE7QFqoDYVGXGFQDaBopt5rXit14rJhlses/j1pVYsstXyOAIQaIXA3Llza8U/mq2ouq3rLCadL98jipUd27qdGte9wPz582tFb4rud7CFQJsK+NzQphdGs3olMFQ/N/QKx05tLbAknxuKJFn5efGmm25q23Mrho7XiuRhrUgclm2N78Crr756rUge1opejrVbb721vq1I9tWKHpG1otdmrViNvFasUl4eUyRpa+edd155jvPmzasViwWVz6+yyiq1Yv7M8vn4t+uEE06oFYnAepximHpZf4Vz9tln14oeleX2iHXYYYfViuH/1ebydssttyy3H3vssbVijtTaIYccUot6ou6FSzNi7rTTTrWi52et6Flau+CCC2rFqvC1yBUs/P2nSNjWioRlbZtttqkVq93X4lzC4bbbbqs3q6+WxZD+2t57710rEq61MWPG1IoEay3eQw899NDyuWIqhFqx6FOtmBu0Vsx9WroUCyXVTjrppFqRlK7Hbbxz/fXX14oEdK1InNevQ9QTdTSjHHTQQWW9EUNZcoFhcehAJV/FHXoC8WtR/LoSq63FnButLDHfSfyytOOOO7bd5NWtPG91d75A/KIZw25isvGYh2golfj/NlYDLRKg5QqcQ+ncB8u53nzzzWVPh1hgoOp1MFjOzXkMboHo9RFzisUiDNHDRSHQKQIxP1+898Yw0mqevk5pu3YSKJJX5WI+8blh+PDhvQKJuTpjuHqRAK33GOzVgR2+U6RuYn7S+I4QK9Q3ljfeeKPs3bnaaquV2xvnCm3cr7v78Z1j8uTJ6YUXXih7X8aq7zF8vqdr0p+YcWz03owh7hFz3LhxPfa6jR6eRUeJcrGimIe0r+e3qPOOnrexGFNjXbHIU6x6327l4IMPTj/72c/KXr4/+MEP2q15HdOerv/XdEyzNZTA4gWKX3HKnarbxR9hDwLtIVC9Zqvb9miVVhDonUC8bhcsWFAO+5EA7Z2ZvdpDoHrPrW7bo1VaQWDxAkXPsHInr93FW9mj/QSKEXup6AVafnboKdnWfi3P36JI1FVD4ReOHsnEZv0AEknU+Ftc6U/MODbKmmuuWf4tLlYkfeOvmWVR59iOyc9mnvNQr8scoEP9FeD8CRAgQIAAAQIECBAgQIAAgSEvYIDwkH8JDGoACdBBfXmH9slVPY/8kji0XwedePbVa7e67cRz0OahK1C9bqvboSvhzDtNoHrNVred1n7tHboC1Wddr92h+xro5DOvXrfVbSefS6e2PRbrqRb7aVx4qVPPR7sJdCdgCHx3Mp7veIGNNtoojRw5Mo0ePbrjz8UJDC2BWJUyVieMW4VApwnEKqExBL76Qt5p7dfeoSsQc4rF0LdiQYShi+DMO1Ig5rArFjDp1ZDVjjxBjR7UAsXiNuXc4csss8ygPs92PbkpU6akr33ta+VcmPFecswxx6SYjzXW7micG7Nd269dBPoiIAHaFy37dpTA8ssvn9Zbb72OarPGEgiB+LARE4ErBDpRYNSoUZ3YbG0mUC6+0N3cangItLNAfG4YO3ZsOzdR2wh0K+AH/25psmzYbLPN0mWXXZYlliAEBlrAEPiBvgLiEyBAgAABAgQIECBAgAABAgQIECDQMgEJ0JbRqpgAAQIECBAgQIAAAQIECBAgQIAAgYEWkAAd6CsgPgECBAgQIECAAAECBAgQIECAAAECLROQAG0ZrYoJECBAgAABAgQIECBAgAABAgQIEBhoAQnQgb4C4rdMYM6cOemJJ55Ib775ZstiqJhAKwRqtVqaNm1aeuWVV1pRvToJtFRg5syZ6dlnn21pDJUTaIXA3Llz0+OPP54WLFjQiurVSaBlAvG5Yfr06Wn27Nkti6FiAq0SmDVrVpoxY0arqu9VvU899VT6+te/nl5++eVe7W8nAgQ6U0ACtDOvm1b3QmDq1KnpoYceSs8991wv9rYLgfYReOGFF9KDDz6YHn744fZplJYQ6KXAvffem+655x4/PvXSy27tI/DYY4+lKVOmSOC3zyXRkl4KvPjii+mBBx4oX7+9PMRuBNpG4L777kuTJk1K8+fPH7A2HXzwwemrX/1qOvroowesDQK3r0Ak6X0va9/r05eWLd2Xne1LoJME3nrrrbK51W0ntV1bh7ZA9Zqtboe2hrPvNIHqdRu3w4cP77Tma+8QFmh87Q5hBqfegQJeux140TS5LjDQr9/LLrss3XbbbWV7fvrTn6YjjzwyjR8/vt6+Jb0TvUnPPvvs9Nvf/jZF54YoSy+9dPrwhz+c/uVf/iVtt912S1q14zIIvP766+ncc89Nl19+ebrrrrvSF77whfJ6dhf6+uuvT9dcc02aOHFiil75Ud797nenD3zgA+mII47wWugOLvPzEqCZwYUjQIAAAQIECBAgQIAAAQIEBlZg3rx56d///d/LRmyxxRZlT+qjjjoq3X777f1u2Morr1z2Kj3ggAPSRhttVNb3ta99LZ100kn9rlsFrRd4xzvekb70pS+l6P0ZSc3Flb322ivF34EHHpguvvjitOKKK6Y77rijvI1jo4ex18LiFFu/3RD41huLMEACyy+/fBm5uh2gZghLoM8CI0aMKI/x2u0znQPaQCBet/GhUe/PNrgYmtAngeo9t7rt08F2JjCAAvG5YdiwYclrdwAvgtBLLBCv22WWWabsHbnElSzhgd/61rfSk08+md73vveVvUBHjx5dJq0igdWssuGGG5bnF/Vtttlm/ao22nreeef1qw4H904g3lOXWmqptMMOO/TugP/b6z3veU95L3p/RhK0sTTztdBYr/u9F9ADtPdW9uwwgY033jiNHTs2Lbfcch3Wcs0d6gIjR45Me+yxR5lEGuoWzr/zBD70oQ+VQ3/iQ6NCoJME4ovJuuuu63NDJ100bS0F4kv27rvvXk+yYCHQSQIxRDiGDOf+4TQWXvrmN79ZUsVQ9eixefrpp6dDDjkkHXfccenjH/94035UiKHvMcdpJHqXtLzxxhtpv/32S3vvvfeSVuG4JRDo6+fZ6hpXtwuHbMZrYeE6Pe69gG8nvbeyZ4cJxK82kp8ddtE0ty6w7LLLlr056k+4Q6BDBOKDXXcf+jrkFDRziAr43DBEL/wgOe343NDXL+qD5NSdRocLDNTnhkhyvvbaa+mf//mf00477VQqfvrTny7n/3zmmWfSGWeckUU2EqPTpk2rx3rkkUe6PI4NMR9lJD/vvvvuss0xLHtRK9b/6U9/Sn/4wx9SJEsXVRYsWJCmT59ebopzv//++7vs9vjjj6dqTtbnn38+TZgwIcUiaz2V/sb885//nG666aZyAcI5c+Z0G+qll14qzz/OvbvSG8vGY1999dWy52/M8xmLHFXn3riP+4NLQAJ0cF1PZ0OAAAECBAgQIECAAAECBAh0IxBzOsYw9+gsE8PgqxI/hEVv0Cjf/va301NPPVVtavrt7373u/SpT30qrbnmmukb3/hGOc/kJptskmII9frrr58OP/zwesxYnCmSk1GuvvrqspfqOeecU9/+61//uuwFHj1YP/OZz5R1/vjHP65vf+CBB9J//Md/lIvyfOc73ynrWGedddLWW2+d/uu//qs02HbbbVOMhIgE7M4775zWXnvttOuuu6aYFuD888+v11Xd6U/MOJ9nn302feITnygXGYofb2KRoYi5cHnwwQfTjjvuWHrccMMNZZs23XTT+sJVsX9fLKv6r7zyyvTe9763vMaRMI75OT/ykY+kSIoqg1dAAnTwXltnRoAAAQIECBAgQIAAAQIECPyfQAy3j4WO4jYWQIq5GhtLJNs++clPprlz59YXSGrc3qz7W265ZXrXu96V/vrXv5bJvFiN/sYbb0z33XdfmQT94Q9/mO68884y3Oc+97l6QjSG6F933XXpxBNPLLddccUVZQLz2muvTXHMPffckyKR+m//9m/lfrFT9IyMHo6RdLz33nvLZGskeGPu00i2/t3f/V2KRGOUSJSefPLJKXpmRrI0ymGHHdZlYaj+w/jvHQAAL99JREFUxgzziLPCCiukI488skw8xvmPGzeuSy/M6PEaidnPfvaz6dJLLy0XkAqTlVZaKX34wx9OV111Vdm+vljGAXFukfCM+T0POuig8n4keW+55ZYUyVll8ApIgA7ea+vMCBAgQIAAAQIECBAgQIAAgf8TiARXJAnHjBlTzvW5KJiYGzQWZ/qf//mfsnfhovbp73Mx53/MMxolemNGsjHWr9hqq63K4e7xfAxnX7hEL9WqxCr2xxxzTJnQjQUoo8Q0RPvuu295vxrGv80225Q9Q+PJSLhGT9FIkMYQ+Fh3YPz48WmDDTYojznrrLPK5GL0TI1V0I8//vjy+dNOO628bUbMSF4+9thjafLkySnqixJzwEaiM5K1Ud58880y8brWWmuVPV7LJ4v/RK/dWAgqEtif//zn0yuvvJL6ahlTAYRXJH+rUiXCF2Ve7eO28wUkQDv/GjoDAgQIECBAgAABAgQIECBAoAeBGN5cJfQiyRk9EBdV3vnOd9aTo9FbtFVzQ8b8p1EWXreiSkZGT8Weym9+85tyFftzzz03fexjH6v/xVDxhVecj0RilFissjGJWtVftWXUqFHVU+XtEUccUd7GMPoozYq5zz77lL1Ooxdm9EqNEsPgYz7jKA899FCZAN5oo43e1t4Yuh/D4J977rl0xx13lPtX7e+NZVzfmTNnplNOOaU8NuYXveiii8r7MaepMngFrAI/eK/tkD+zeEOMX5biF6/u/nEb8kgA2lIgJjqPX6ZjNeL4Uwh0kkAMoYrXcPQmUAh0kkAs+PDHP/6xnBMtVtVWCHSKQMxfF0NF40t99CBTCHSSwJQpU8qFfWKo86ISc808l1NPPbVMmkXSLYa591RiePwFF1xQ9pL8yU9+UvZO7Gn/Zm6rHKKX48Kl2hbPx3ydUf7zP/8zRaKwFSUSp/FdukrGNitm9N6MuTtjLtG49rEAVcxHGj1vo0QCNEr0RF1UiSRotCVeP7vvvvuidimfq7wWtoxEacyr+t3vfrccUv+P//iP5f4L79dtxTZ0pIAEaEdeNo3ujUAkQOPXnL/85S8SoL0Bs0/bCMTQlHjdxlCQwZwAjdUrb7vtti6/qj/99NPldYjhLDHJe2OJ4S0xR5HS3gKxYEAMLYr5mKwG397XSuu6ClSfG1544YUkAdrVxqP2Foh/T+NzQ3zRlwBt72uldW8XiM8NkcSPv6r339v36v8zTz75ZJnsipri82ZvfqiNeUCjnHDCCeU8ke3QqaZK6EW7YiX3KI8++mjLEqBRfyxSVA0Rb1bMVVddNUVP1ZiL9KSTTkoXXnhh2RM0EqKxGFI1pD/+bV5UicWZoizpZ83vf//7KRLi8X0jXgvxb78y+AUkQAf/NXaGBAgQaEuBY489tvxlfVGNe+aZZ+rzIjVuv/7669Oee+7Z+JT7BAgQIECAAAECBHoUiKRnjFCJEp8z46+3pZ061TQmQGPV9igxV2kMKV+4PPHEE2ViOYaLL2mJH1iiY0LMFRqlWTFjsafoxRk9bffff/8ywXz77benSEx+/etfT5tvvnkZL3p4xnyg0TGksUTSN8rCQ/0b9+nufgzjj8WXzjzzzF4lwrurx/OdJyAB2nnXTIsJECAwKARi6FF8oIwPNVWJYScx5CV+FY6hfI0leoD25tf6xmPcJ0CAAAECBAgQIPDBD36wHPIcCb2+lhiGvfDn0r7UUc0h2viZt6fjFzUMu5rbcvbs2fVDd91113LI+MUXX1wOIY/HVYkRZbFw0Y9+9KPqqSW6vfLKK8vjYtX2KM2KGSvNV6O93vWud6UJEyak6NV51113lXFiJfvtttuufHzjjTemj370o+Xz8Z/wmThxYtp4441TXNeeyqIsq7iN26pRaNW16qnOnrZVx3d3rRe3vae6beu/gARo/w3V0KYCsZpedNFfffXV27SFmkVg0QKrrLJKWmONNcrVKRe9x+B4dpdddknxpwwugRgiFT0slnRI0uDScDadJBCfG6KXS7z/KgQ6SWDllVcuX7f9SdB00vlq6+ASiM8N8Z2tGvLcyrNrXPW7lXEa6541a1aXnqeN22JYfpQXX3yx8ek0derU8nHMTV2Vqu2XX355Ouigg8rE4F577VWu1B5DuWOEVKyivv3225dDyWN4+XXXXVcOX486ZsyYUVZVza1Z1bvw7Y9//OP6AlBxTCwUFAshVZ0QYk7QWB2+vzHjHK+99tpy4aaqDTHUvkriRk/X888/v0yCHnfccWmnnXYq5+qMfWPO40gEX3LJJfXXTV8st9hiizLkOeeck+J+JMVjlFkspBRzgU+aNCmNGDGi7F06ffr0ct/Kr3zQw3+qRGoM3V+452pPr4UeqrSpmQJF1lshkE3g6KOPjpmca8Vkw9liCkSAAAECBAgQIECAAAECg0OgSJKV3ylvuummtj2hIqlWK5KHtSJxWLY1vgMXHXNqRfKwVvRyrN166631bUWyr1b0iKwVvTZrxWrkteLHjPKY4ge52nnnnVee47x582rFYkHl80VniVqR4CyfL3oU1oo5SmtFArkepximXtZf4Zx99tm1okdluT1iHXbYYbViwdVqc3lbzN1ebi+mqKoViwrVDjnkkFrUE3UvXJoRs0ho1oqen7WiZ2mtWGyqVgzhr0WuoEgadglXJGxrRZKyVixsXCtWu6/FuYRDsY5Afb++WhY/dtb23nvvWpFwrY0ZM6ZWJFhrxXyvtUMPPbR8brfddqsViz7VzjjjjFqRoC9dVlpppVoxV2mtSErX4zbeKRKotcMPP7xWJE7r1yHqiToW91porKe7+0XSu6w3YihLLjAsDm1mQlVdBHoSiF+Lvve975UTUBdvcD3tahsBAgQIECBAgAABAgQIEOgiUCSWUszjWCRA6z0Gu+wwSB9E6mbatGnlgkTRW7GxxCJS0btztdVWK7c3zhXauF9399/3vvelyZMnl4sBxbGx6nsMMV947s3G4/sTM46NXr8zZ84sY44bN67sddlYf+P96OH58MMPp+gJG/OQ9vX8Guuq7kfP25h2q7Gu6I3cDotdVW2sbg8++OD0s5/9LBUJ0PSDH/ygetptHwW6/l/Tx4PtToAAAQIECBAgQIAAAQIECBAg0FqBSNRVQ+EXjhTJxFhUqBklkqjxt7jSn5hxbJSYXzX+FldiqoT4a2ZZ1Dm2Y/Kzmec81OtaaqgDOH8CBAgQIECAAAECBAgQIECAwFAXMEB4qL8CBvf5S4AO7uvr7AgQIECAAAECBAgQIECAAAECixSIxXqqxX4aF15a5M6eJNDBAhKgHXzxNL1ngfj1qpjMuOedbCXQpgLFROepmGC8TVunWQS6F1iwYEGKeZ0UAp0m4HNDp10x7W0U8LmhUcP9ThLwuWFgr9aUKVPS/vvvX86FGUPMjznmmPSd73wn6Qk6sNdF9NYImAO0Na5qbQOBRx99NE2dOjXtuOOOadSoUW3QIk0g0DuB2bNnp1tuuaWc5yYmJFcIdJJAsSpnev3111OxgmgqVtfspKZr6xAXiM8M8dlh++23T2uttdYQ13D6nSRQrGicbr755lSsqNy0OQA76fy1tbMFbr/99hQLz8Tnhp4W3Onss2zf1m+22Wbpsssua98GahmBJgr4ZtJETFW1l0DV+7O6ba/WaQ2B7gWq12x12/2ethBoP4F43UYP0BhOpRDoJIHqPbe67aS2a+vQFojen1G8dof266BTz37OnDlp/vz5KXqCKgQIEGilgARoK3XVTYAAAQIECBAgQIAAAQIECBAgQIDAgApIgA4ov+CtFKiGXhpK0UpldbdCoHrtVretiKFOAq0SqF631W2r4qiXQLMFqtdsddvs+tVHoFUC1Wddr91WCau3lQLV67a6bWUsdRMgMLQFzAE6tK//oD77jTbaKI0cOTKNHj16UJ+nkxt8AmussUbaYostUtwqBDpNYPz48eUwtuoLeae1X3uHrsCGG26YVlhhhTRmzJihi+DMO1Jg1VVXTVtuuWVabbXVOrL9Gj20BbbZZpty7vBllllmaEM4ewIEWi4gAdpyYgEGSmD55ZdP66233kCFF5fAEgsMGzYsjRs3bomPdyCBgRSw6NxA6ovdH4ERI0ak9ddfvz9VOJbAgAjE54axY8cOSGxBCfRXwA/+/RV0PAECvRUwBL63UvYjQIAAAQIECBAgQIAAAQIECBAgQKDjBCRAO+6SaTABAgQIECBAgAABAgQIECBAgAABAr0VkADtrZT9CBAgQIAAAQIECBAgQIAAAQIECBDoOAEJ0I67ZBpMgAABAgQIECBAgAABAgQIECBAgEBvBSRAeytlv44TmDNnTnriiSfSm2++2XFt1+ChLVCr1dK0adPSK6+8MrQhnH1HCsycOTM9++yzHdl2jR7aAnPnzk2PP/54WrBgwdCGcPYdJxCfG6ZPn55mz57dcW3XYAKzZs1KM2bMAEGAAIGWC0iAtpxYgIESmDp1anrooYfSc889N1BNEJfAEgm88MIL6cEHH0wPP/zwEh3vIAIDKXDvvfeme+65x49PA3kRxF4igcceeyxNmTJFAn+J9Bw0kAIvvvhieuCBB8rX70C2Q2wCSyJw3333pUmTJqX58+cvyeGOIUCAQK8FJEB7TWXHThN46623yiZXt53Wfu0dugLVa7a6HboSzrwTBarXbXXbieegzUNToHrNVrdDU8FZd6JA9ZqtbjvxHLR56ApUr9vqduhKOHMCBFotsHSrA6ifwKIETj/99PTDH/5wUZua9ty8efPKXxKXW265tMwyyzStXhURaLVADL+MoZhLL710GjFiRKvDqZ9AUwVi6pF4DXvfbSqryjIIxDDi+Bs2bFj5lyGkEASaIhCJo5jyKT4zrLvuuk2pUyUEcgm8+uqr5XvvCiuskJZaqnf9s5566qmyeYceemiK4xQCg13A9FLNucISoM1xVEsvBcaOHVvuGXPExZ9CgAABAoNT4I033hicJ+asCBAg0KYCMf/9o48+2qat0ywCzRd4+umnm1+pGgm0scBmm23Wxq1r/6YNK37prrV/M7VwMAlE7yBzvAymK+pcCBAg8DeB8ePHp9deey1ddtllafnll//bBvcIECBAoCUCkydPTscff3zadttt00UXXdSSGCol0E4CMdIkkp9SGe10VbSl1QKjRo0q3+dbHWcw168H6GC+um16buuvv36btkyzCBAgQKC/AsOHDy+r2G233dLIkSP7W53jCRAgQGAxAtV0OSuuuGLaZJNNFrO3zQQGh8Dmm28+OE7EWRAgkE2gd5NsZGuOQAQIECBAgAABAgQIECBAgAABAgQIEGiegARo8yzVRIAAAQIECBAgQIAAAQIECBAgQIBAmwlIgLbZBdEcAgQIECBAgAABAgQIECBAgAABAgSaJyAB2jxLNREgQIAAAQIECBAgQIAAAQIECBAg0GYCEqBtdkE0hwABAgQIECBAgAABAgQIECBAgACB5glIgDbPUk0ECBAgQIAAAQIECBAgQIAAAQIECLSZgARom10QzSFAgAABAgQIECBAgAABAgQIECBAoHkCEqDNs1QTAQIECBAgQIAAAQIECBAgQIAAAQJtJiAB2mYXRHMIECBAgAABAgQIECBAgAABAgQIEGiegARo8yzVRIAAAQIECBAgQIAAAQIECBAgQIBAmwlIgLbZBdEcAgQIECBAgAABAgQIECBAgAABAgSaJyAB2jxLNREgQIAAAQIECBAgQIAAAQIECBAg0GYCEqBtdkE0hwABAgQIECBAgAABAgQIECBAgACB5glIgDbPUk0ECBAgQIAAAQIECBAgQIAAAQIECLSZgARom10QzSFAgAABAgQIECBAgAABAgQIECBAoHkCEqDNs1QTAQIECBAgQIAAAQIECBAgQIAAAQJtJiAB2mYXRHMIECBAgEAnC4wcOTItu+yy5V8nn4e2EyBAoFME4n03ysorr9wpTdZOAgQIECCQXWBYrSjZowpIgAABAgQIDEqBRx55JM2ZMydts802g/L8nBQBAgTaUWDChAlp8803T6NHj27H5mkTAQIECBAYcAEJ0AG/BBpAgAABAgQIECBAgAABAgQIECBAgECrBAyBb5WsegkQIECAAAECBAgQIECAAAECBAgQGHABCdABvwQaQIAAAQIECBAgQIAAAQIECBAgQIBAqwQkQFslq14CBAgQIECAAAECBAgQIECAAAECBAZcQAJ0wC+BBhAgQIAAAQIECBAgQIAAAQIECBAg0CoBCdBWyaqXAAECBAgQIECAAAECBAgQIECAAIEBF5AAHfBLoAEECBAgQIAAAQIECBAgQIAAAQIECLRKQAK0VbLqJUCAAAECBAgQIECAAAECBAgQIEBgwAUkQAf8EmgAAQIECBAgQIAAAQIECBAgQIAAAQKtEpAAbZWsegkQIECAAAECBAgQIECAAAECBAgQGHABCdABvwQaQIAAAQIECBAgQIAAAQIECBAgQIBAqwQkQFslq14CBAgQIECAAAECBAgQIECAAAECBAZcQAJ0wC+BBhAgQIAAAQIECBAgQIAAAQIECBAg0CoBCdBWyaqXAAECBAgQIECAAAECBAgQIECAAIEBF5AAHfBLoAEECBAgQIAAAQIECBAgQIAAAQIECLRKQAK0VbLqJUCAAAECBAgQIECAAAECBAgQIEBgwAUkQAf8EmgAAQIECBAgQIAAAQIECBAgQIAAAQKtEpAAbZWsegkQIECAAAECBAgQIECAAAECBAgQGHABCdABvwQaQIAAAQIECBAgQIAAAQIECBAgQIBAqwQkQFslq14CBAgQIECAAAECBAgQIECAAAECBAZcQAJ0wC+BBhAgQIAAAQIECBAgQIAAAQIECBAg0CoBCdBWyaqXAAECBAgQIECAAAECBAgQIECAAIEBF5AAHfBLoAEECBAgQIAAAQIECBAgQIAAAQIECLRKQAK0VbLqJUCAAAECBAgQIECAAAECBAgQIEBgwAUkQAf8EmgAAQIECBAgQIAAAQIECBAgQIAAAQKtEpAAbZWsegkQIECAAAECBAgQIECAAAECBAgQGHABCdABvwQaQIAAAQIECBAgQIAAAQIECBAgQIBAqwSWblXF6iVAgAABAgTaQ2DWrFnp+eef77Yxw4YNSyuvvHJae+210/Dhw7vd769//Wt65plnut3e2w0rrbRSeve73/223d966630v//7v+nBBx9M06ZNSxtttFEaP3582nrrrdNyyy2X3njjjfTNb34znXjiiW871hMECBBoF4EpU6akWq3WbXOWWWaZ8v125MiR3e7T6vfbxsDR1nvvvbd8741/K+bMmZPGjh2bNthgg/JvnXXWqe9+8cUXp+233z6tt9569efcIUCAAAECnSAwrPgHr/t/nTvhDLSRAAECBAgQ6FFg4sSJ6b//+7/T5ZdfnmbOnNntvpH8HD16dJl4/NznPpf23XfftPTSf/ut9Pzzz0+HHXZYt8f3dsNuu+2Wfv3rX3fZPRIGhx56aLrrrrtSJGTf//73pwULFqR4Ptqw5557pj//+c/piSeeSDNmzOhyrAcECBBoJ4GTTz45/f73v08TJkzosVnxY9A73/nOtOuuu6ajjz46jRs3rr5/K99vqyAvv/xyOu2008p/H5577rny6REjRqT3vve9KbY99thjKX6YGjNmTNpll13K7Zdeemm67rrryjZX9bglQIAAAQKdIGAIfCdcJW0kQIAAAQL9ENhhhx3S97///XT//fenZZddtl7TgQcemKJ36J/+9KfyC+2pp56a1l133XTLLbekAw44IK2//vplj8zqgHnz5pV340v7F77whXTVVVelO+64Iz3yyCPlXzxflZ/+9Kflc9Gr6MYbb0zf/e53y8RqbK/qqfaNhOYHPvCBMvl51FFHpSeffDLdeeedadKkSWXP1UgM/OpXv0q/+93vysdvvvlmdahbAgQItJ3ASSedlH7zm9+U75ONjbvnnntS9Oy8++67009+8pP0r//6r2n69OnpnHPOSRtuuGH61Kc+lebPn18eUr1PNvv9tmrPlVdeWfbuPPPMM1MkP4844og0efLkNHv27PK9+NFHH02vvPJKmcSNNsSPaPEXPfGjh6hCgAABAgQ6TiB6gCoECBAgQIDA0BAoevbEyI/yr0hKvu2kiy/ftSOPPLK+T/HFt3bfffeV+51xxhm1ondmrfhi/7bj4olVVlmlftxvf/vbt+1T9CiqFT2JakXvzi7b9t577/K4hZ9v3Ommm26qFcNGy/2KhGnjJvcJECDQlgJFr/v6e2Ixzcgi21gkRcv3xep9+eCDD64VvS5rrXq/jUacd955taWWWqpsWzH1Se2GG25YZNuqJ4uEZ+3www+vn8svfvGLapNbAgQIECDQMQJ6gHZcylqDCRAgQIDAkgusuuqqPR4cw83PPffctMcee5T7RQ+ggw46qLw/d+7cctjjRz7ykR7r6G5jzHd33HHHdekB+tprr5W9O+OYrbbaqrtDy7jRSyrKs88+2+1+NhAgQKBdBBb3fhvtjHmOY3qSqkQvy1/+8pepFe+3EeP2228ve3vG0PaYi7T4sar+fl+1YeHbGBYfowiqfxf0AF1YyGMCBAgQ6AQBCdBOuEraSIAAAQIEmiRQ9Pqp1xRzbXZXYk66qjz88MPlUPn4Qr7XXntVTy/R7T777NMlARoLHhU/G5d13XzzzT3WGXOEjho1yhygPSrZSIBAuwj09v122223TUUP+nqzb7311jIB2uz325g+5POf/3z9PTfux2JzvSnx78WPfvSjchqV+LdAIUCAAAECnSbwt5UNOq3l2kuAAAECBAi0TCDmDW0sf/jDH9JnP/vZtMYaazQ+3ef7sfp79HCqSmMSNhbciHk+P/ShD1Wbu9xGb6WYI08P0C4sHhAg0OECkSiNhd+KqT7KM4n5mi+88MKmv99G7/4HHnigjLHaaqulmKu0LyUWbIp5S/UA7YuafQkQIECgXQQkQNvlSmgHAQIECBBoI4FYiKixbLDBBl1WKG7c1tf78UW/KltssUV6xzveUS6sEc994hOfSFdffXXacccdq1263J544onp9ddf7/KcBwQIEOh0gcb33Hi/jUWRmlGq99votfnVr361XuV+++2XejNEv37A/90p5iYte6cu/LzHBAgQIECg3QX+Ng6u3VuqfQQIECBAgEA2gViFvSox7HzcuHHVw6bextxyX/7yl+t1vvDCC2mnnXZKX/nKV7oMla92KBYSSWuuuWb10C0BAgQ6XuCll15KU6dOrZ/HdtttV7/frDsxlUms8F6V9dZbr7rbp9uYy3mttdbq0zF2JkCAAAEC7SAgAdoOV0EbCBAgQIBAGwk89dRT6Yorrqi36JOf/GT9fivuHH/88WnPPfesVx3z1J166qmpWLE+FSvO1593hwABAoNR4Oyzz67PyxlzgTa+HzbrfB966KEuVY0dO7bLYw8IECBAgMBgFzAEfrBfYedHgAABAgT6IPDHP/4x7bbbbunpp58uj9p3333TWWed1Yca+r5rDIG/7rrryl6fMbyyWhTp8ccfL9ty4IEHlivTNy4S0vcojiBAgEB7CcRK7NHb/fTTTy8btsIKK6Trr7++JT3up0yZ0uXke9Or/7XXXks33HBDl+MWfvBP//RPqXEu54W3e0yAAAECBNpFQAK0Xa6EdhAgQIAAgcwCV111VXr55ZdTfAmPoeex0NHEiRPLx7FY0WGHHZaOPfbYNHz48Ja3LBYBOe2009Iee+yRDj/88PTII4/UY/785z9PsSpyLAry93//9/Xn3SFAgECnCMTCQTHdx4orrli+786YMSNNmDAhzZw5My299NLpYx/7WLk9VoRvRYkftxrLuuuu2/hwkffjfXn+/PnpnnvuSeedd16XuT933XXXFPOIKgQIECBAoFMEJEA75UppJwECBAgQaLLAc889VyYWFyxYkFZaaaVyyHkMvdx6663T7rvvnuLLb+4S839GIvbMM89M3/jGN+oLHj3zzDNlcvSiiy5KBxxwQO5miUeAAIF+CcQPTffee2+ZUIyK1l577fSZz3wmxY9N//AP/5DGjBnTr/oXd/Dyyy/fZZcXX3xxsfMpxxzN8X4bf/HvQTUaIOYBjcXqYrtCgAABAgQ6RUACtFOulHYSIECAAIEmCxxxxBHpqKOOanKt/a8uhsTHsNAYWnnIIYeku+66q6w0ErUHHXRQWmedddLOO+/c/0BqIECAQCaB6Pl50003ZYr29jCjR4/u8mQsurTJJpt0ea6nB+95z3vqm2P4vORnncMdAgQIEOgQgfxdOzoERjMJECBAgACBgRXYdNNN0+9///v0xS9+sd6Q6EX1//7f/6s/docAAQIEFi+w1VZbddmpcdX5Lhu6eRC9Pquy3HLLVXfdEiBAgACBjhGQAO2YS6WhBAgQIEBgcAnE/KOx4EdPJebGO+ecc7qsinznnXemV199tafDbCNAgACBBoGPfvSjXeZzXnhRpIZdF3m3caGjxvuL3NmTBAgQIECgDQUkQNvwomgSAQIECBAYCgKR3IwV3l955ZXFnu7xxx/fZZ+YE1QhQIAAgd4JrL766mnfffet7/yLX/wiPfHEE/XH7hAgQIAAgcEuIAE62K+w8yNAgAABAm0qsMIKK5TzyJ177rmLbWHjXHUxR2jMQacQIECAQO8FYnG5au7O119/PR1zzDG9P9ieBAgQIECgwwUkQDv8Amo+AQIECBDoi0B86a3Km2++Wd1tyu2S1L3BBhukU045JT3yyCM9tuHJJ5+sb99yyy3TsssuW3/sDgECBNpRYEneE3t7HktS97rrrpsuuOCCckX3iHPNNdekX/7yl70NaT8CBAgQINDRAhKgHX35NJ4AAQIECPRN4C9/+Uv9gFmzZtXv9/fOnDlz0ty5c+vV9Lbu9ddfP82bNy/ttttu9dXe65U03Dn//PPLR8OHD0/f+ta3Gra4S4AAgfYUaHy/jak+GpOW/Wnxkr7fRswDDjggxftp9KSPst9++6XjjjsuzZ8/v3zc3X9mzJjR3SbPEyBAgACBjhCQAO2Iy6SRBAgQIECg/wL33Xdfeuyxx+oV/epXv0rxRboZ5ZJLLulSzRVXXJFixfbFlegBGiXm9Nxpp53SWWedlaZPn14/7LXXXitXfY8v7LHwxhlnnJF23nnn+nZ3CBAg0I4C0cN+4d6Vl112WVOauqTvt1XwQw89ND388MPp4x//eKrVaimGxu+www7pwgsvTA888EBasGBBuWvcTpo0KZ144onpq1/9avncOuusk0444YSqKrcECBAgQKBjBIYV/+jVOqa1GkqAAAECBAj0WeDaa69N3/72t9PEiRPf1stn1VVXTdtvv3364he/2GWl9d4G+fSnP50mT55c/i18zJgxY9I222yTYgGj7bbbbuHN5eNLL700ffnLX05f+cpX0v3335+irTHcfdSoUWn06NFp6tSpZa+pOD5Wg3//+9+/yHo8SYAAgXYR2H///cv326eeeuptTdpqq63SFltsUSYb37ZxMU/09/12UdXfcsst6eSTTy7ff2fPnl3ustxyy6VYNCl68r/xxhtprbXWSrvssku5iNI+++yTYgE7hQABAgQIdJqABGinXTHtJUCAAAECg0jg+eefT08//XQaP358/axeeumlshfStGnT0tixY1MsgLT22mvXt7tDgAABAs0XePbZZ9Ojjz5a/vAU8yxH4jMWnNt0002bH0yNBAgQIEAgs4AEaGZw4QgQIECAAAECBAgQIECAAAECBAgQyCdgDtB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD4BCdB81iIRIECAAAECBAgQIECAAAECBAgQIJBZQAI0M7hwBAgQIECAAAECBAgQIECAAAECBAjkE5AAzWctEgECBAgQIECAAAECBAgQIECAAAECmQUkQDODC0eAAAECBAgQIECAAAECBAgQIECAQD6B/w84NbfehVnKfwAAAABJRU5ErkJggg==" width="672" /></p>
<pre class="r"><code>coefplot(m_time2,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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GiCovqFpvgjQdqzo127dgESoCgIIIAAAggggAACCCDgigABUFdqinwigAACCCDgI4HzzjtPdu7cWSZHTZo0kczMTNm2bZsZhqTMBryBAAIIIIAAAggggAACCERZgABoFMB/97vfycMPPxz2kY4dO2b2WbhwoZx11lll9t+4cWOZ93gDAQQQQAABBBBAAAEEEEAAAQQQQAABBP5PgADo/1lE7Cd97G/Tpk3VTj8rK6tG+1f7wOyIAAIIIIAAAggggAACCCCAAAIIIICA4wIEQKNQgZMnT5ZGjRrJjTfeKCdOnJBvfvOborPhVrVs375dVq1aZcZYO//886vanM9LCZw8eVIyMjLMoPB16tQp9Sm/IuBfAZ1UZseOHaJjLDZu3Ni/GSVnCJQjUFhYWM67vIWA/wVycnIkPT3dXDeEcp3m/xKRw3gR0OsGHZKkRYsWDD0SL5UeoHLq2OH5+fmSmpoaoFJRlHgQ4LrBvVquOgrnXpl8meMJEybI0KFD5frrr5d9+/bJn/70pypnDJszZ46ZBX7w4MGiM8KzhCewefNm2bVrlyQmJkpaWlp4O7M1AjEUOHTokKxdu1ZSUlJkyJAhMcwJh0YgfIHTp0+bnew6/BTYA4HYCGzZssXcfKpXr565+RybXHBUBMIXOHLkiKxZs0Zat24tw4YNCz8B9kAghgIrV66U3Nxc0371yUkWBFwR0PHu9aU3TTt16uRKtuM6n7XjuvRRLrwG4RYtWiRXXnmlnHvuufKHP/whyjmIr8PZXkh2HV+lp7QuC9g2a9cul4W8x68A7Td+697Vkts2a9euloN8x5+AbbN2HX8ClNhlAdtu7drlspD3+BKwbdau46v0bpaWAGiU66127dpy++23y3vvvScPPvigaM9QvWvLggACCCCAAAIIIIAAAggggAACCCCAAALeCxAA9d40pBQHDBgg2t2/ZcuW0rdvX9MzNKQd2ShkgYYNG5pt7TrkHdkQgRgLNGjQwOSAthvjiuDw1RKoVauW2Y+xl6vFx04xFLDnXLuOYVY4NAJhCeh1g557abthsbGxTwS03eqj74y97JMKIRshC9hzrl2HvCMbxkyAMUBjRi/mIuXpp5+WefPmyTXXXCPXXnutPPDAA2bMyhhmKzCH7tmzpxmLo379+oEpEwWJD4Hk5GQZM2aM6Dh0LAi4JmADn/rEAwsCLgl0797djP3JdYNLtUZeVUAnWx09erQJIiGCgGsCw4cPF53Iy14/uJZ/8hu/At26dTNzjXDd4E4b4NuJD+rqiiuukNWrV8u6devM2KDr16/3Qa7cz4LeCedk5H49xmsJdPIu25MuXg0ot5sCtFs3641ciznnct1AS3BVQK8buPHkau3Fd7615yeTH8V3G3C59Fw3uFV7BEB9Ul9t27aVd999V2644QYZMWKEPP744+ZOmE+yRzYQQAABBBBAAAEEEEAAAQQQQAABBBBwUoBH4H1UbdprZtq0aXLxxRfL1VdfLQcOHPBR7sgKAggggAACCCCAAAIIIIAAAggggAAC7gkQAPVhnemkSCtWrJBZs2bJjh07zGPxPswmWUIAAQQQQAABBBBAAAEEEEAAAQQQQMD3AgRAfVpFOpbEfffd59PckS0EEEAAAQQQQAABBBBAAAEEEEAAAQTcEGAMUDfqqSiXjz76qJnZfOrUqUXv8UP5AhkZGfLRRx9JdnZ2+RvwLgI+FcjLy5OPP/5Ydu/e7dMcki0EKhY4ffp0xR/yCQI+Fti/f7+5bjhx4oSPc0nWECgrkJ+fb64bdu7cWfZD3kHA5wI6AfBnn33G/Bc+ryeyV1ZAhyzUeENmZmbZD3nHlwL0APVltZSfqWPHjskdd9whepEzZ84cufbaa82ESeVv7d27x48fl08//dSTP0rbt283GdM09YTRoEEDady4cbmZzc3NLXEyadasWYUzBGp6GjTSpU6dOtK8eXPRAOjRo0fl8OHDkpSUZD4rLCw0v585c8b8HsnjlzcTMsfHX9tjVe1P/6/rdhpIsjMLhtv+aX+1zP/x4v/w/y86///UWZeCgoLi/IJ/dPz17x///6v3/99eN+i6SZMmRe2X829411+0v+q1P9vgqnP9q9fBet2g9p06dbJJFa05/3L+DeX6UxtMddqf7me/f1Xn/7/e8Nfvt/rSa+TiwSTOv5x/ExIStImVWcr7/l+d9lc84XDbvw5XqPGGXbt2SZ8+faJ6/VU83/wcugAB0NCtYr5lcnKypKammnFB9UTQtWvXqORpypQp8uqrr3p6rL/85S9yzjnnSO3ateXSSy+VunXLNsV//vOfoic2u6SkpMiQIUPsr0Vr7anxwQcfFP2uPwwcOLDE7/aXrVu3yoYNG+yvET1+Wlpa0XHsDxwf/1Dbn7YZDYR+8sknpvmE2/5pf/z/s+cdu47W+ccer/Q6WsfX8z/tn/Zfk/anX2hycnKKkuD8G971F///Yvf/z3YGKGq8//mB8y/Xn6Fef9b0+xf//2P3/5/rn9hd/2knLw3WR6v9lz7H83voAmWjTqHvy5ZRFtBg4Ycffigvv/yyXHLJJdK2bduo5OA73/mOnDx50vTeqekBly1bJllZWabXZ6tWrcxa7xiWt3To0EH27dtnPtK7OeWdUPRD7cXZvn17c8dSf9dgqvaA0R6mpZfWrVvLkSNHisqivU8jdfzSx9bfOT7+obY/bS/16tUzvZCq0/51/9IL7Y/2F2r7q8n5t3S7s7/T/mh/0Wh/+ve/vCWc9qdpaE8kXTj/hn/9VVP/mpx/vKh/F4+v7VU7DZTXoUDrI5z272L5tYz2+l9/Lr1Qfv7+uPL3h/9/Nfv+X/r/vv4eyf//hw4dMjdMtaNaNP/+lFdO3gtNoNa/u5l/9SxwaNuzFQI1EhgwYICsWrVKxo4dK6+//nqN0qpqZw2ebtmyRfSY9hH4qvbhcwT8IKA9OFasWGEC+3ohxIKASwKNGjUyYy/rl3G9IGRBwBUBvXG6adMm6d+/v2g7ZkHAFQENgC5fvtx0FujYsaMr2SafCBiBL7/80lw3DBo0qNxHiGFCwK8CBw8elI0bN5onWysa1s+rvF933XUyd+5cefHFF81QiF6lG2/p0AM03mo8jsqrj6zpiwUB1wQSExNl+PDhrmWb/CJgBCrqVQ8PAn4X0F4i+mJBwDUBfWKE6wbXao38WoHevXvbH1kj4JSAPtGqLxZ3BJgF3p26IqcIIIAAAggggAACCCCAAAIIIIAAAgggEKYAAdAwwdgcAQQQQAABBBBAAAEEEEAAAQQQQAABBNwR4BH4GNaVTiz0zjvviM7MuGfPHklPT5fMzExp0aKFtGzZUjp16iQXXnihmdGcRwpjWFEcGgEEEEAAAQQQQAABBBBAAAEEEEDAWQECoDGouvfff1+ef/55MwnQiRMnqsyBTiIxYcIEmTFjhvTo0aPK7dkAAQQQQAABBBBAAAEEEEAAAQQQQAABBL4S4BH4KLYEnV10/Pjx8vWvf93M3hVK8FOzp71Cn3vuOenVq5fcdNNNcurUqSjm2t1DnTlzRnJyctwtADmPa4Hc3FwpLCyMawMK76aAnntZEHBRgOsGF2uNPFsBrhusBGvXBPS7bX5+vmvZJr8IGAHiDW41BHqARqm+Dh06JCNHjpRNmzaZI3bo0EG+9rWvSffu3aV9+/bSsGFD86pfv74J2mVlZYm+NPip+3z22WfmEfmnnnpKtm3bJm+99ZbojI8sFQts3LhRNm/eLOeddx6zs1XMxCc+FND/90uWLJGOHTvKOeec48MckiUEKhY4ffq0+ZAAfsVGfOJPAb1m0GuHoUOHSps2bfyZSXKFQDkC+p1BnzDT7xQDBgwoZwveQsC/AkuXLpXs7GwZPXq0MOybf+uJnJUV0KEM169fL+eee660bdu27Aa84zsBAqBRqpIpU6aYsT5vueUW+cEPfiB9+/YN+8h79+6VX/7yl/LEE0/I7NmzZfr06WGnEU872Lsxdh1PZaesbgvYNmvXbpeG3MebgO0BagOh8VZ+yuuugD3n2rW7JSHn8SagvT91oe3GW80Ho7w6L0ZBQYF5ypEAaDDqNF5KoW1XF8697tS4bx6Bz8jIcEctzJxu2LBBFixYYHp0/fa3v61W8FMP2a5dO3n88cdl/vz5cv/99/N4bJj1wOYIIIAAAggggAACCCCAAAIIIIAAAvEn4IsAqI6F+cwzzwRW/9NPP5Uf/vCHcv7553tSxlGjRplZ4nfs2OFJekFNpHbtr5o3dxKDWsPBLZdtu3Yd3JJSsiAL0H6DXLvBLJtts3YdzFJSqiAK2Gtd2m4Qazf4ZbLt1q6DX2JKGBQB22btOijlCnI5fPEI/EsvvST2kbkgYuvkR2effbanRRsyZIjs2bNHunbt6mm6QUqsR48ekpycLCkpKUEqFmWJA4GWLVuanuK6ZkHANQH7RdyuXcs/+Y1fAR2XPSkpSVJTU+MXgZI7KdCsWTPp16+fNG/e3Mn8k+n4Fhg4cKDk5eVJQkJCfENQeucEunXrJg0aNJC0tDTn8h6vGQ47ALpw4UK5/fbbPfM6fPiwCeTdfffdnqXpt4T0P8SHH37oWbY0WLx69WpzoeNZogFMSCeW6tKlSwBLRpGCLlCrVi3p3Llz0ItJ+QIqwF3wgFZsHBRLv8RwYzkOKjqARdTrhk6dOgWwZBQpHgS44R8PtRzMMuoE1lw3uFW3YQdA9e7imjVrAt1j0+sq1N6akydPlksvvVTGjh1b4+Tvvfdec4esadOmNU6LBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiyQNgB0DZt2sjQoUPlk08+kcTERGnbtq1U9zE37cmoj4frGKBBXvSuwOWXXy7jxo2TiRMnmvFAL7jgAqlbN3R+nU33/fffl4ceeki0F+5TTz0VZDLKhgACCCCAAAIIIIAAAggggAACCCCAgCcCoUfgih3usssuk/z8fFm+fLnoIxc1WTQIOmPGjJok4cS+Tz/9tKxbt07+9re/mVeTJk3MuKA6boQ+sqJjTukj2/r41alTpyQ7O1tOnjwp6enporPIr127Vo4cOWLKeuedd8rUqVOdKDeZRAABBBBAAAEEEEAAAQQQQAABBBBAIJYC1QqAjhkzxgTrahr81IJrGj/96U/lySefjKVDxI+tY5ssWbLE9P5866235Pjx47Js2TLzCvXgGhydPn263H///aHuwnYIIIAAAggggAACCCCAAAIIIIAAAgjEtUDt6pS+f//+MmzYsOrsWmafffv2ic5cOGnSpDKfBe0NHS7gzTfflPnz58vVV18tjRs3DqmIrVq1knvuuUd27dpF8DMksa820h6027ZtEx0+gAUBlwS0Z/z27dslKyvLpWyTVwSMQGFhIRIIOCmQk5MjW7duNU/iOFkAMh23AnrdsGPHDsnMzIxbAwrursDBgwfNU4/uloCcx6sA1w3u1Xy1eoBqr02d2MeLZenSpfLnP/9Z/vrXv3qRnBNpaA9afeXm5sqiRYtky5YtkpGRIRoM1gsXDZR26NDBvNq3by+DBw82j8Y7UTgfZXLz5s0maKxj1aalpfkoZ2QFgcoFDh06ZIa9SElJ8excW/kR+RQB7wTsTSe79i5lUkIgsgJ6PaZBpHr16plrsMgejdQR8E5Ah8nSSWpbt27tWScV73JHSghULrBy5UrzvVjbb0JCQuUb8ykCPhLQzlb60rlddFhDFv8LVCsA6mWxGjVqJK+++qq89957Mnr0aC+T9n1a9evXFx1PlSUyArYXkl1H5iikioD3ArbN2rX3RyBFBCIvQPuNvDFH8FbAtlm79jZ1UkMgcgK2zdp15I5Eygh4L2DbrV17fwRSRCAyArbN2nVkjkKqXgpEJACqvRlffPFF+fzzz0V7MuXl5ZWbZ53oR++266KPhsdbALRcFN5EAAEEEEAAAQQQQAABBBBAAAEEEEAAAc8EPA+A/utf/5Lzzz/fPHocTi4vuOCCcDZnWwSqFGjYsKHZxq6r3IENEPCJgE54pkvQ2+5vfvMb+fnPf15inF4dx0xvmtWuXds8hlq8SpKTk82wIX369Cn+Nj/7TMBOkFinTh2f5YzsIFC5gD3n2nXlW/MpAv4R0OsGPffSdv1TJ+QkdAFtt3r9p48RsyDgkoA959q1S3mP17x6fpYZP368CX7qxD3Nmzc3JzKdzKNLly4ljPUkp70/e/fuLTfeeKPofiwIeCnQs2dPMxaHDjXAgoBLAhro03GCdRy6IC865vGJEyfKLaKOH6kDixdfbHC0+Hv87D8BG/jUIDYLAi4JdO/e3Yz9yXWDS7VGXlVAhxTTJ+kYP5H24KLA8OHDTQDUXj+4WAbyHJ8C3bp1M3ONcN3gTv17+u1EH2nXiWc++OADOXDggGzcuFHWrVsnl19+ubz22mvmZ/1dX+vXrzcDxmqQ9LrrrhNOeO40GldyqnfCORm5Ulvks7SATt5le9KV/iwov991111m0Hv922FfOnSKLnoDw75n11lZWTJw4MCgFD+w5Qh6uw1sxVEwc87luoGG4KqAXjdw48nV2ovvfGvPT4L38d0GXC491w1u1Z6nAdAvvvhCxo4dK6UfZ//BD34gjz32WBkZneF82LBh8sMf/rDMZ7yBAAIIIBB8Af3Cpo+N2Jd9/F+/xNn37JpHo4LfHighAggggAACCCCAAAIIIBAJAU8DoDr7VZs2bcrk8+KLL5alS5fK4cOHy3w2atQo+fOf/ywrVqwo8xlvIIAAAggggAACCCCAAAIIIIAAAggggAACNRHwNADauXNn8/h76Qzp43CTJ0+WCRMmSH5+fomP9ZF5XebOnVvifX5BAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRqKuBpALRdu3Zy/Phxueiii2TWrFnmsXc7wYU+5r5p0yYz2dGyZctMIPSjjz6S++67z5Rh586dNS0L+yOAAAIIIIAAAggggAACCCCAAAIIIIAAAiUEPJ0FXnt6/uIXv5Arr7yyqCeozgCv43/quG4PPPCA3HDDDfL3v//dDNKtj8zbRSe8YEHAS4GMjAzZsmWLmTQlKSnJy6RJC4GICuTl5cny5cvNbMQdOnSI6LFIHAGvBU6fPu11kqSHQFQE9u/fb27WDxgwwMyqHZWDchAEPBDQJ+w+++wzMxtxp06dPEiRJBCInoBOjqwTXg4ePDjwE4BGT5UjRUPATvx9zjnnSHJycjQOyTFqKOBpD1DNy7hx4+SJJ56QevXqmRPYiBEjirKoj8E/9NBD5vfiwc9WrVrJ9OnTi7bjBwS8ENAA6NGjR8sde9aL9EkDgUgJHDt2zLTb9PT0SB2CdBGImID9+15QUBCxY5AwApEQsNcNhw4dikTypIlAxAT0CTyda4HrhogRk3AEBXbv3i16/i09VF4ED0nSCHgisG/fPhNv4LrBE86oJOJpD1Cb45tuukmuvfZayczMNHci7fu6/slPfiJ9+vSRN998U1avXi1nn3223H333eVOnlR8P35GAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTCFYhIAFQzoV2AK+oGPHr0aNEXCwIIIIAAAggggAACCCCAAAIIIIAAAi4J6BCQLG4JeB4A3bFjh+hs8CwIxFpAJ+XS8WRatGgR66xwfATCEmjatKm0bNlSUlNTw9qPjRHwg0Dt2l+NrpOQkOCH7JAHBEIW0OuGrKwsc/4NeSc2RCAGAmfOnBF92aVx48bSvHlz0TZshyGxn9lzsv2dNQJ+E+jYsaP5zqZD6LEg4JJASkqK6NBlOqQjixsCngZAdeIDHQN0xYoVUreup0m7oUkufSWgJyR9sSDgmkBiYqIMHz7ctWyTXwSMQJ06dZBAwEmB1q1bi75YEPCzwLRp08wEs6HmUTsCrFy50kysGOo+bIdANAV69+4dzcNxLAQ8E9DAJ8FPzzijkpDnkyB98cUXZozPZ555Rk6ePBmVQnAQBBBAAAEEEEAAAQQQQCDoAnqTSV/as9O+ij+Gad+za922+OdB96F8CCCAAAIIVCTgeQBUD9SkSRO59957zWMYN998s6xZs6ai4/M+AggggAACCCCAAAIIIIBACAIPP/ywnDp1SvTJO/tavHix2fOiiy4qes9+tn//fmnfvn0IKbMJAggggAACwRbwPAB69dVXy6effip79uyRN954w8wEP3ToUBk2bJg8//zzkpOTE2xRSocAAggggAACCCCAAAIIIIAAAggggAACvhHwNACqj1g88sgjpnD6qMWFF14oL774oqSnp8s111wjjz76qOkVeuutt8q6det8g0BGEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCYAp4GQJWoTZs2ZaSaNWsmt9xyi+j4oAsWLJC8vDw577zzzCQfL7zwAr1Cy4jxhhcCOjsmPY69kCSNWAjk5uaWmck1FvngmAiEK1B8ZuJw92V7BGIpwHVDLPU5thcCnH+9UCSNaAvokA75+fnRPizHQ8ATAeINnjBGLRHPA6BV5bxdu3amF2jjxo1l2bJlMnnyZElNTTW9Q6val88RCEdg48aNsnDhQjl48GA4u7EtAjEXyMzMNDeLGD855lVBBqohoOPO6VJYWFiNvdkFgdgJbN682Vw36JiJLAi4JGAnnj1x4oRL2SavCBiBpUuXyqJFi8z4tZAg4JLA1q1bzXVDRkaGS9mO67zWjUbp9UvQ/PnzZc6cOfLOO++UOLk1b95cvve978n48eOjkRWOEUcC9m6MXcdR0Smq4wK2zdq148Uh+3EmYHsg2UBonBWf4josYM+5du1wUch6nAnY3nOcd+Os4gNSXA3gFxQUmMm9dEg9FgRcEbA3n7hucKXGRDwNgOof3VmzZsldd91lBPbu3SvPPvus/P73v5fdu3eXUBk4cKDoDPHf/va3pUGDBiU+4xcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMALAU8DoJoh7eWpAU3tyv7222+bOzk2o4mJiXLVVVeZwOeQIUPs26wRiIhA7dpfjfDAncSI8JJoBAVs27XrCB6KpBGImADtN2K0JBwhAdtm7TpChyFZBDwXsG1WJ6FlQcA1Adt+7dq1/JPf+BWwbdau41fCnZJ7HgDVXp+33357CYGOHTvK1KlTZcqUKdKqVasSn/ELApES6NGjhyQnJ0tKSkqkDkG6CEREoGXLltK3b1/RNQsCrgnYm0527Vr+yW/8CnTv3l2SkpLM2PTxq0DJXRTQuRV00fbLgoBrAvpkqE6SnJCQ4FrWyW+cC3Tr1s10/ktLS4tzCXeK73kA1BZd70BecsklprfnZZddJnwRsjKsoyXQsGFD6dKlS7QOx3EQ8ExAz5+dO3f2LD0SQiCaAtwFj6Y2x/JSQJ9g6tq1q5dJkhYCURGwPT/r1o3YV7uolIODxKcAN/zjs96DUOr69etz3eBYRUbkr+SoUaPk4Ycflj59+jjGQXYRQAABBBBAAAEEEEAAAQQQQAABBBBAIEgCngdABw8ebGZ6p8dnkJoJZUEAAQQQQAABBBBAAAEEEEAAAQQQQMBNga9mifEo7/rY229+8xsed/fIk2QQQAABBBBAAAEEEEAAAQQQQAABBBBAoGYCngZAdfyZjIwMmThxosyfP79mOWNvBBBAAAEEEEAAAQQQQAABBBBAAAEEEECghgKeBkAzMzPl2muvlVdeeUVuu+22GmaN3RGomcDJkydl27Ztcvr06ZolxN4IRFngzJkzsn37dsnKyorykTkcAjUXKCwsrHkipIBADARycnJk69atcurUqRgcnUMiUH0BvW7QhbZbfUP2jJ3AwYMHJT09PXYZ4MgIVFOA64ZqwsVwN08DoPqlx85CeMUVV8SwWBwaAZHNmzfLunXrTK9kPBBwSeDQoUOydu1a+fLLL13KNnlFwAjYm052DQsCrghs2bJF1q9fL3v37nUly+QTASOgnVB0yc7ONmv+QcAlgZUrV8qKFSukoKDApWyTVwRMZyu9btizZw8ajgh4GgBt2rSpTJo0SerXry8zZ84MmUD/WM+ePTvk7dkQgVAEbC8kuw5lH7ZBwA8Cts3atR/yFOk8HD16VO68804ZPXq0OdTGjRtl+PDh8uKLL4rt2RLpPJC+twLx1H69lSO1WAnYNmvXscoHx0UgXAH7d9Kuw92f7RGIpYA959p1LPPCsREIR8C2WbsOZ1+2jY2ApwFQLcIjjzwiOhP88uXLQy7Rpk2bRHs8sSCAAAIIxJ+A9nTt27evPPDAA/Kvf/3LAOiXuGXLlsn1118v+kRBXl5e/MFQYgQQQAABBBBAAAEEEEAAAU8E6nqSyn8S0XFndNzF5557Tm666Sb529/+JjfeeKM0b9683MNopHznzp2m18+3vvWtcrfhTQSqK9CwYUOzq11XNx32QyDaAg0aNDCHjIe2q+OcXnrppZU+OvLWW2/JrbfeKs8880y0q4LjVUPADoVTp06dauzNLgjETsCec+06djnhyAiEJ5CYmGh24Lwbnhtb+0NAz7l647tuXU9DE/4oHLkItIC9XrDrQBc2IIXz9Cxz/Phx6datm+Tm5hbxPPvss0U/V/YDAdDKdPisOgI9e/aUTp06mSEZqrM/+yAQK4Hk5GQZM2aM1KtXL1ZZiNpxf/vb38ru3burPN7vfvc7+fGPfyy9evWqcls2iK2A/QJeu7bnD5nEtmAcPfAC3bt3lw4dOnDdEPiaDl4B7Y3TRo0aBa9wlCjwAjrkkQZA7fVD4AtMAQMjoLGvtLQ0rhscqlFPv520aNFCrrrqKoeKT1aDLKC9kHQ8WhYEXBTQ3hy2J52L+Q81z6+99lqom8obb7wR8rZsGDuBeGi3sdPlyJEU4LohkrqkHQ0Bzr/RUOYYXgtoz8+EhASvkyU9BKIiQLwhKsyeHcTTHqCaq8mTJ8sf//hH6dy5s2ivTg2KVtadXcf+/POf/+xZgUgIAQQQQMAdge3bt4ec2XC2DTlRNkQAAQQQQAABBBBAAAEEEAi8gOcB0AsuuEA6duwo77zzjpx11lkhAX7jG98Ia9KkkBJlIwQQQAAB3wskJSWJzgAfyqLbsiCAAAIIIIAAAggggAACCCAQroCnj8DrwXXMr9tuu0169OgRcl6GDRtW4URJISfChggggAACzgno+T/UJZxtQ02T7RBAAAEEEEAAAQQQQAABBIIv4HkAVMl0oorKJj9Yvny5ZGZmFunqLPE/+tGPin7nBwQQQACB+BC45ZZbQipo+/bt5YorrghpWzZCAAEEEEAAAQQQQAABBBBAoLhARAKgegCdEf7BBx+Ua665Rm6//fbix5ScnBzp37+/PPTQQyXe5xcEvBTIyMiQjz76SLKzs71MlrQQiLhAXl6efPzxxyHNjh7xzET4ACNHjjQ3zSo7jI4j/cILLzCpWWVIPvrs9OnTPsoNWUEgdIH9+/eb64YTJ06EvhNbIuADgYKCApML/Y7FgoBrAuvXr5fPPvvMzATvWt7Jb3wLHDhwwFw3FO/cF98i/i99RAKgixcvlj59+sgdd9whL730kmzevLmEhH7hnT9/vsyZM8cESPXLPgsCXgtoAFTHFjx8+LDXSZMeAhEVOHbsmGm36enpET2OXxJ/5JFH5Je//KU0aNCgTJa6du0q+jfloosuKvMZb/hToLCw0GTMfiH3Zy7JFQJlBex1g07QyYKASwL2Zj/fqVyqNfJqBXbv3i16/s3Pz7dvsUbACYF9+/aZeAPXDU5Ul8mk5wFQPYGNHTtW9uzZU6mCjhH64Ycfyuuvvy4zZ86sdFs+RAABBBAItsCMGTNMj9dZs2aZgrZs2dJMprdhwwbRm2YsCCCAAAIIIIAAAggggAACCFRXwPMA6PTp00UfHWrYsKGMGjVKrrrqqgrz1q5dO9MD9LHHHjPd3ivckA8QQAABBAIvoEHPcePGmXK2atVKLr30UklISHC63Nqb4eTJkyVe2lOn9Hv6Oz0mna5qMo8AAggggAACCCAQRwK1atWKo9IGo6ieB0AXLVokAwYMMD1AFyxYIFdffXWlUvqovD4up4/KsyDgpYAG2HWCrRYtWniZLGkhEHGBpk2bigYDU1NTI34sDhA5gWeffdaMW5qUlCTFX40aNSrxu/1MhwB4++23I5ehKKVsJ0F0PXi9ZcsW6dWrl/l/qP8X7atx48ZSr149adu2bdF7+plO1PXMM89ESZnDRELAXjfo+ZcFAZcE9O+ILvXr13cp2+QVASPQsWNH0fOv/m1lQcAlgZSUFBNv0I4bLG4I1PUym3v37jWTH82ePVuaNWsWUtI6RqMua9asCWl7NkIgVAE9IemLBQHXBBITE2X48OGuZZv8lhLQQKcGy4r37Dxz5ozk5uaaLUuPeaq/69MTri916tRxvQgm/zp+tAZBK5rUScd9Kr1s3bq19Fv87pBA69atRV8sCLgmYG84EQB1rebIrwr07t0bCAScFNDAJ8FPt6rO0wCoXjTqF7i+ffuGrLBs2TKzrU76wYIAAggggEBQBCZNmiT6Kr5kZWVJcnKyCYwyY2RxGf/9PHToUNFB7XV4guLLhRdeaAKjS5YsER3P3C7a85WbblaDNQIIIIAAAggggAAC/hLwNABat25d6d+/v6xbt06GDBlSZUnfeOMNee+998x2gwYNqnJ7NkAAAQQQQAABBKIloMNR6Kv4Ynta6U1ffWSPBQEEEEAAAQQQQAABBPwv4PkYoBMmTJB77rmn6BG/igjmzZsnkydPLvpYe1qwIIAAAggggAACCCCAAAIIIIAAAggggAACXgp42gNUMzZt2jT5xz/+ISNHjpSpU6eKnRlLH/vbtGmTbNiwQZ5++mmxj77rPpdccolcf/31+iMLAggggAACCCCAAAIIIIAAAggggAACCCDgmYDnAVANeL7wwgsyceJEmTJlismoPhqvY56Vt+iM8a+88oroNiwIeClgJxspPdGIl8cgLQQiJaAT5ehsmHZG7Ugdh3QR8FpAz70sCLgowHWDi7VGnosLcP4trsHPrgicOnVKCgsLmQXelQojnyUEcnJyzDw4Jd7kF98KeP4IvJZUZ7199913Zf78+dKvXz/Rk1rppWfPnjJ37lxZvny5NGnSpPTH/I5AjQU2btwoCxculIMHD9Y4LRJAIJoCOjnOggULZM2aNdE8LMdCwBMBO2u6fplhQcAlgc2bN5vrhv3797uUbfKKQNFkbSdOnEADAecEli5dKosWLRJ7/eBcAchw3Aps3brVXDdkZGTErYFrBY9ot8sxY8aIvvTxdw1G7dy5U9q2bWtmTdXJA1gQiKSA3o3Rxa4jeSzSRsBLAdtm7drLtEkLgUgL2B5IfJGJtDTpey1gz7l27XX6pIdApATy8/NN0px3IyVMupEUOHnypBQUFJhOU3Xq1InkoUgbAU8FtO3qwnWDp6wRTczzHqAa5MzLyyuRae0ROnjwYPNY/IgRI6R48PNHP/qRaG8nFgQQQABWt2vxAABAAElEQVQBBBBAAAEEEEAAAQQQQAABBBBAwGsBzwOgw4YNk1WrVoWcz+uuu07GjRsn9s5lyDuyIQJVCNixE7mTWAUUH/tOwLZdu/ZdBskQAiEI0H5DQGITXwnYNmvXvsocmUGgEgHbZu3ks5VsykcI+E7Atl+79l0GyRACFQjYNmvXFWzG2z4SiOgj8KGUc+DAgaLjfujM8NoblAUBrwR69OhhJt9KSUnxKknSQSAqAi1btpS+ffuKrlkQcE3A3nSya9fyT37jV6B79+6SlJQkqamp8YtAyZ0U0KftdNH2y4KAawIaD9AnSBMSElzLOvmNc4Fu3bqZCZDS0tLiXMKd4nveAzScoh89elQeeOABc8JbvHhxOLuyLQJVCjRs2FC6dOkifAmvkooNfCagPTg6d+5sJpTzWdbIDgJVCnAXvEoiNvCpQIMGDaRr165cN/i0fshWxQK252fdujHv21JxJvkEgQoE9IY/N54qwOFtXwvUr1/fXDdw7vV1NZXIXI3/Sk6aNEleffXVokR18O2LL75YbCPQP8jlvXJzcyU7O7toP2aCL6LgBwQQQAABBBBAAAEEEEAAAQQQQAABBBDwSKDGAdCXX35ZHnvsMfnZz35mZm/TfIU7C5Y+rvG9733PoyKRDAIIIIAAAggggAACCCCAAAIIIIAAAggg8JVAjQOgmsxtt90mgwYNkssuu0yOHTsm/fv3N2MvVoasj8i1b99edLyl8ePHS69evSrbnM8QQAABBBBAAAEEEEAAAQQQQAABBBBAAIGwBTwJgOpRhw8fLn//+99l1KhR8uSTT8rQoUPDzgw7IIAAAggggAACCCCAAAIIIIAAAggggAACXgp4OgmSBkHnzZsnbdu29TKPpIVAtQROnjwp27ZtEx2XlgUBlwTOnDkj27dvl6ysLJeyTV4RMAKFhYVIIOCkgA7htHXrVjl16pST+SfT8Sug1w260Hbjtw24XPKDBw9Kenq6y0Ug73EqwHWDexXvaQBUi3/JJZdIx44dw5IoKCiQRYsWhbUPGyNQlcDmzZtl3bp1kpGRUdWmfI6ArwQOHToka9eulS+//NJX+SIzCIQiYG862XUo+7ANAn4Q2LJli6xfv1727t3rh+yQBwRCFsjMzDTbFp9gNuSd2RCBGAusXLlSVqxYUTSfSIyzw+ERCFlAO1vpdcOePXtC3ocNYyvgeQC0OsVZuHChfPzxx9XZlX0QqFDA9kKy6wo35AMEfCZg26xd+yx7ZAeBkARovyExsZGPBGybtWsfZY2sIFCpgO0BateVbsyHCPhMwJ5z7dpn2SM7CFQoYNusXVe4IR/4RsCzMUCLl0gbwCeffCIbNmyQTZs2VTorvG6jwc+ZM2cWT4KfEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBGgt4HgDVMTyuvvpqHmmvcdWUTEDHs2zYsGHJN/mtUgHrZdeVbsyHCPhIoEGDBiY3tF0fVQpZCVmgVq1aZts6deqEvA8bIuAHAXvOtWs/5Ik8IBCKQGJiotmM824oWmzjNwE952rv5bp1PQ9N+K2o5CdgAvZ6wa4DVrxAFsfzs8y1115L8DMCTUXHspw1a5Z8/etfl+uuu06aNWsWgaMEK8mePXtKp06dpH79+sEqGKUJvEBycrKMGTNG6tWrF/iyUsDgCdgv4LVr+2KUneABU6KICXTv3l06dOjAdUPEhEk4UgL2xmmjRo0idQjS9UggNzdX3nvvvTLjXf7rX/+ShIQESUlJKXEk/R4zatSoQAcHdSJlDYDa64cSAPyCgI8FunXrJmlpaVw3+LiOSmfN0wDosWPH5P333zfHmDRpkowdO1aGDBkijRs3Ln1c87vOVKgDzf+///f/yv08iG/qhE///Oc/5YsvvpDjx4/L2WefLf37969y4qhzzz1XnnvuORk0aJDMmDFDJk6cKH/84x+DSORZmbQXEsFPzzhJKMoCtjdHlA/L4RCosYDtAVrjhEgAgSgLcN0QZXAO57kA51/PST1P8Ne//rXcddddYaX7xBNPyE033RTWPi5tTM9Pl2qLvJYWIN5QWsTfv3saAM3Pzxcd/1ODdX/5y19CKrne5frFL34hr7/+ekjbu7yRznB3ww03yJo1a8oU46qrrpIHHnhAunbtWuYz+0aLFi1k/Pjx8vDDD8uLL75IANTCsEYAAQQQQAABBBBAAAEEfC5wxRVXmFmjtVOMXU6fPi3z5s0TfXJCOxAVX/SG+OjRo4u/xc8IIIAAAtUU8DQA2rp1axP87Ny5c1jZ6devn4S7T1gH8MHGq1atkqFDh5Z53MFm7a9//asJAt93332mh6d9v/S69GMRpT/ndwQQQAABBBBAAAEEEEAAAf8J9O3bt0xHoZycHDPXgwY7X331Vf9lmhwhgAACARHwNACqJvrou45VqY+3h9OdfevWrTJgwICAsJYsht7h056f9k6fju8yZcoU8zi7jhmxZcsW0QDpggUL5I477jCPx//hD3+Q8h6B1X1ZEEAAgSAIbNiwQV566SXz5IAtz6FDh8yPBw4ckDvvvNO+bdY6nMptt90mdqyzEh/yCwIIIIAAAggggAACCCCAAAIVCHgeAL311ltNF34dr/LGG2+s4LAl39YJft58883ABkDtmJ9a6rZt28rChQulT58+RQj2sQa9+6ePtz/44INmsOs33niDyY6KlPgBAQSCJqDDn+hwHuUthw8fNsOClP6sV69eoo+PsSCAAAIIIIAAAggggAACCCAQqoCnAVAdA1R7M+pj3N/+9rdNL1B9LL6yZdeuXeYLcOnxTirbx7XPNMBrl6eeeqpE8NO+r2vt1aQTQn3/+9+X7373uzJy5EgTLNWgKUv4AhkZGaY9Dhw4UJKSksJPgD0QiJFAXl6eLF++3MxGrDMSB3W5++67zURwOvZV8UUniWvZsqWkpqYWf1uSk5Pl0ksvLfEev/hPoHR9+i+H5AiB8gX2798vmzZtMjfkmU27fCPe9aeAfcpMO1OwIOCawPr16yU7O1sGDx4sTOTlWu3Fd371ibWNGzfKOeecY76nxLeGG6X3NACqf3T1MXYNhOpy8803h6wQ5ABoenq6cdAv75dffnmVJjrO5/z582XmzJkyYsQIEwStbHKkKhOM0w00AHr06FHRnmQEQOO0ETha7GPHjpl2W6dOHRMEdbQYVWZbz2szZsyocjs2cEtAJ0PUxX4hdyv35DaeBex1gw7FQQA0nluCe2XX4JEuegOVBQHXBHbv3m3iBxpDKG8IONfKQ37jR2Dfvn0m3qDXDRrrYfG/QG0vs9ikSRP5xje+4WWSgUirR48ephw60VOod7V0FkB9FP5//ud/5KKLLjKzBQYCg0IggAACCMiZM2dQQAABBBBAAAEEEEAAAQQQiJKApz1ANc/jx4+Xt99+WyZMmGBmhK9ssgr9AqjR8rlz50apuLE5jM7+rgFNHR5AHw3UXl2hLjpZkk6UdNlll8lrr70W6m5shwACCCDgYwF6R/q4csgaAgg4LTBnzhzzXaT4jSbtGakT7+mwMs2bNy9Rvk6dOslvfvObsK7PSyTALwgggAACcSkQaue2uMTxaaE9D4Dq5BRdunQxM/uGOmP5+eefLx9//LFPiWqeLe0Bes0115ixThcvXmwmOAonVZ0k6eWXX5arrrrKjI0Szr7xvG27du3MeDItWrSIZwbK7qBA06ZNyx0D08GikOVSAlu3bjXv6JdxvQGoY50GbdEbfrqEeg0QtPJTHncF9LohKysrkP8v3a2V8HOu4+3rWNLlLXv27Cnztp6zdAz+Nm3alPnMlTfsUE/169d3JcvkE4EigY4dO5rvbPXq1St6jx8QcEFAhy7UoctatWrlQnbJ478FPA+A6l3V2bNnh/XF5+KLLxb9wh/kRWc7/vTTT2Xy5MmycuVK0f8s4SznnnuuvPXWWwwxEAaaGofrHEbybIpAxAR0/KPhw4dHLH0Sjp2ADmtiF/3C/fTTT9tfA7MO5ymHwBSaggRCQCfurGryzkAUNOCF0CfRVq1aVaKU77//vjz66KNmWKnp06eX+Kx9+/ZOBz+1MPaGEwHQElXLL44I9O7d25Gckk0ESgpo4JPgZ0kTv//meQBUCxzuLL1653XQoEF+t6pR/nQ2Yw2Afuc73zG9OPVRm3HjxplH40NN+KyzzpJ//OMfcskll8i2bdtC3Y3tEEAAAQR8ILBw4UJ59913i3Ly+9//Xm666Sbp27dv0Xv8gAACCCBQMwG95tZX8eXIkSPmVzusVPHP+BkBBBBAAAEE4kOgWpMgXX/99Z5O4KCPBP7v//5v4MW1l6vO7q5jeS5dutSMlxpuoXWcIg2CcqcsXDm2RwABBGIncOrUKfnxj39cIgM6JvRtt91W4j1+QQABBBBAAAEEEEAAAQQQ8F6gWj1Ac3JyZNeuXaLBOC+WTZs2eRpQ9SJPkUxj8ODBNRrLs23bthWObRTJfJM2AggggED1BJ588kn58ssvy+z8wQcfyKuvvlqtG2JlEuMNBBBAAAEEEEAAAQQQQACBcgWq1QNUU7rvvvvk5MmT5SYazptHjx6Vxx9/PJxd2PbfAnXrVit2jR0CCCCAQJQFDh8+LPfcc0+FR/3pT38qubm5FX7OBwgggAACCCCAAAIIIIAAAjUTqHYU7bnnnpPnn3/eDPraoEGDauXixIkTol8Mz5w5I+edd1610oi3nXQA98cee0zGjBkTyMkzvKxPbVcaVKhu+/QyL6SFQLgC2nZ1Nkw7o3a4+7O9fwTuuusu0Zt9FS07d+6Uhx9+WO68886KNnHqfT33siDgogDXDS7WGnkuLsD5t7gGP7sioMMEFRYWmuteV/JMPhGwAvp0NPEGq+H/dbUDoFo0PVHt37/f/6UMSA6PHTsmd9xxh+Tn58ucOXPk2muvlREjRkSldNnZ2Z4MU6Bj3umibUf/2OlswbVq1aqwDLqNXSrr9arp6UsXTU/T3bhxo2zevNkE14vPzqZ5sBeIkTy+zXfpNcfHv6r2l5mZKUuWLJEOHTrI1772NdOEwm3/pdud/Z32F932t27dOnO+tv4VrX/xi1/IDTfcIO3atSvapCbnv6JESv0QjfrXY+hiz8nFsxCN41c2Cz3Hj277L173+rPf/fWaQa8dhg4dKi1atCjKPuffir8ulHf9VQRX6odY179mR/Mb6evPUsUu+jWS5ddOJbrYddFBi/0QyePrYez1d7FDlviR43P+rej6V+fG0O+ao0ePNt/hXL3+4fqjTon/88V/Cer//y1btpjrBp3Qu/Tke5Euf/H0+Tl0gYqvaEJPgy2jJJCcnGz+Y+3YsUMSEhKka9euUTmyTtLh9TAFOu7r22+/LfXr1zez2pf3B+Of//xniQB7586dy50tWXvKLV682FzQKohegA0bNkz0bowudq0/7969W1atWqU/miWSxy8edLXH4/j4h9L+bJvV9qIvXcJt/7S/Vva/XdE6Fv//pk2bZoI+RZmo4Ae98NcbXH/84x/NFjU9/8Wy/u0XHL3YLb7Ewp/j/58A/lX//bHn3vXr10tWVlYRHuff8K6/Ynn+0eu/8o6vT5zpkp6eHvHrz/KOH+n/f2vXrjXlKx44Mm/8559IH18PY6+/Y1F+ju+2vw6rV1BQYL7LLV++vEbf/2h//rj+/c+px6zi4fyzYsUK04M5Wu2vuC8/hydQ7QCoVu6UKVOkY8eO1e7yq3cpNWr+7LPPhpfrON1aH4X98MMP5eWXXzZBQ50MKRqL9oLQ4Gt5vXnCPb7+gdN0tDeFBj01AKkXTOUtiYmJZhv9TLfRx4HLW/Qz3dZ+6dZ0ywuo6r56XA0e27JwfPz92v5sW9e2TPt38///66+/bm7O2Lqsaj137ly5+eabZciQIeacZs9jrtV/ReXk/MvfH1f+/mo+Xf3/Z/Nd+v9hvP//s8PJ6PlUjYJ2/VfRtYxtB/Fe/5Sfvz+u/P3h+69b3/9tz1b9uxLNv7/23M46fIFa/w4ahT1Y18SJE+V73/ueXHrppeEfsZw9HnnkEXOn/e677y7nU94KksCAAQNMD8yxY8eKBgciuaxevVp27dolVXVJj2QeSBuB6ggcPHhQli1bJikpKSYYVp002Ce2Anl5edK7d2/Zvn17WBnR4Ocnn3xS4Y2hsBKL0cYNGzY0Pe+PHDkizZo1i1EuInfYs88+W7788kvRXoJaxyzBEVizZo3oUzb9+/c3Q5AEp2SURHvXf/e735XrrruuqKd9kFTmzZsnem2t19mff/55kIoWF2XR3uf6t1PHEfRikmHX0BYsWGDmbfjmN79pOqq4ln/yG78COtTVtm3bpF+/ftKpU6eIQujfL+0s8eKLL5qhECN6sAAnXu1Z4EeOHOkZy/nnn+9ZWiSEgBXo0aOHGT9Rg0gsCLgk0LJlSzPcA8EVl2qtZF51wrpwg5+awqeffmoubkqm5tZv9g64XbuVe3IbzwLdu3eXPn36VDqOVzz7UHb/CjRu3NhkLikpyb+ZJGcIVCAwcOBA02FFe2myIOCSQLdu3cx1Q1pamkvZjuu8VusR+Ouvv168/AN71llnmbs+cV0TFN5zAb2T2qVLF8/TJUEEIi2gj7LpmHMsbgpkZGTIrFmzqp15HQv0yiuv9PTvbLUzU40d7aOm1diVXRCIqYD2vorW+OoxLSgHD5yAfQReH/VmQcA1Ab3xz4KAiwI6nArXDW7VXLV6gP7Xf/2Xp6XUu5b0AvWUlMQQQAABBGIkMHPmzEpn4q0qW3v37pUHHnigqs34HAEEEEAAAQQQQAABBBBAIEQBbhOGCBWJzXSMl3feeUe2bt0qe/bsMTNTZmZmik46pHfCdByJCy+8UPSxAB4ljEQNkCYCCCDgrYDOXmpncq9Jyjo29ve//316AtcEkX0RQAABBBBAAAEEEEAAgf8IEACNQVN4//335fnnnzeTAJ04caLKHOgM7BMmTJAZM2aIjmvJggACCCDgT4GjR4/KQw89VGHmcnNz5c477zSzvFfVy1MfpWcohAop+QABBBBAAAEEEEAAAQQQCFmAAGjIVDXf8MCBA/Lf//3f8tprr4WVmPYKfe6550zQ9Ic//KE8/vjjwhg/YRGyMQIIIBAVgVGjRom+KlqysrJMALRevXoyffr0ijbjfQQQQAABBBBAAAEEEEAAAQ8FCIB6iFlZUocOHZKRI0fKpk2bzGYdOnQwM5TrjKPt27cXnbBHXzqQbk5OjuiXZH1p8FP3+eyzz8wj8k899ZRs27ZN3nrrLdEv0CwVC+gQA9qDSocSYAiBip34xH8CZ86ckR07dkirVq3Ezuzqv1ySIwTKFygsLCz/A95FwOcCev2Vnp5urhu40ezzyiJ7JQT0ukGXU6dOlXifXxBwQeDgwYOSn58vqampLmSXPCJQJMB1QxGFMz8QAI1SVU2ZMsWM9XnLLbfID37wA+nbt2/YR9aJMX75y1/KE088IbNnz6b3UBWCmzdvll27dplHTdPS0qrYmo8R8I+A3jBZu3atpKSkyJAhQ/yTMXKCQAgCp0+fNlvZdQi7sAkCvhDYsmWLufmkN5j1RjULAq4IaIcJXbKzs13JMvlEoEhg5cqVokMEtW7dWhISEore5wcE/C6gHdP0pTdNtdMVi/8FCIBGoY42bNggCxYskCVLltRotvt27dqZx98vu+wy+fa3vy3Tpk2T2rVrR6EEbh7C9kKyazdLQa7jUcC2WbuORwPK7L4A7df9Ooy3Etg2a9fxVn7K666A7QFq1+6WhJzHo4A959p1PBpQZjcFbJu1azdLEV+5JnoWhfr+9NNPRcfuPP/88z05mo4vp7PE6yOyLAgggAACCCCAAAIIIIAAAggggAACCCBQsUBMAqDLly83Y1tWnK1gfaKTH5199tmeFkofi92zZ4+naQYtMR1TVRe7Dlr5KE9wBRo0aGAKR9sNbh0HuWS1atUyxWPs5SDXcjDLZs+5dh3MUlKqIAokJiaaYnHeDWLtBr9Mes7VR98Zezn4dR20EtrrBbsOWvmCWJ6IPQJ//Phxefrpp2XNmjVmQONf/epXRX46WGz//v3NjOg//elPi94P6g86/uSHH37oWfH08ZbVq1dLv379PEsziAn17NnTjMWhE0uxIOCSQHJysowZM4aJzlyqNPJaJGC/gDNESxEJPzgioBNT6tifXDc4UmFks0jA3jht1KhR0Xv8gIArAsOHDxf9fmuvH1zJN/lEoFu3bqKxHq4b3GkLEekBunjxYunTp4/ccccd8tJLL4lORlN80dnQ58+fL3PmzJFrrrlG8vLyin8cuJ+1t+Yf/vAHeeONNzwp27333mvukjVt2tST9IKaiPZC4mQU1NoNfrm0N4ftSRf80lLCIAnQboNUm/FVFq4b4qu+g1hazr9BrNXgl0l7fjL5UfDrOaglJN7gVs163gN09+7dMnbsWDlx4kSlEj169DC9IjVq3qZNG3nkkUcq3d7lD7t27SqXX365jBs3TiZOnGjGA73gggvC6uavs+m+//778tBDD8nChQvlqaeecpmEvCOAAAIIOCxw8OBB+fnPf15uCfQpD110oj6dTbv0Mn36dNFrABYEEEAAAQQQQAABBBBAIFoCngdA9YuNBj91HIQRI0aI9lKsqIenzmquPUAfe+wxM6v5ueeeG61yR/04OhzAunXr5G9/+5t5NWnSxIwLqgHgTp06SVJSkjHTR1hOnTol2dnZcvLkSUlPTxedRX7t2rVy5MgRk+8777xTpk6dGvUycEAEEEAAAQRUQIe50ac4Klv0yYfylkmTJhEALQ+G9xBAAAEEEEAAAQQQQCBiAp4HQBctWiQDBgwQXTdr1kzmzZtnHv+uqAT6qHxhYaF5VD7IAVCdtX3JkiWm9+dbb71lvjwuW7ZM9BXqosFRDTDff//9oe7CdggggAACCCCAAAIIIIAAAggggAACCMS1gKdjgO7du9cE9mbPnm2Cn6HIHj161GymkyUFfWnbtq28+eabZvzTq6++Who3bhxSkVu1aiX33HOP7Nq1i+BnSGJshAACCCCAAAIIIIAAAggggAACCCCAwFcCnvYAbd26tWgvxb59+4bsa3tAHjt2LOR9XN9QZ3fWV25urukpu2XLFsnIyJB9+/ZJZmamaKBUZyHVV/v27WXw4MHG1fVyRzv/aqq2AwcONEMMRPv4HA+B6grosCHLly8vOg9UNx32QwABBBAIXWD//v2yadMm8yQTs2mH7saWsRcoKCgwmbBjMMc+R+QAgdAF1q9fb4Z/0++8TOQVuhtbxl7gwIEDsnHjRjnnnHMkOTk59hkiB1UKeBoA1Rnc+vfvb8a61JnPq1p0VvT33nvPbDZo0KCqNg/c5zpj2GWXXRa4cvmlQBoA1R7Ghw8fJgDql0ohHyEJ6A0hbbd16tQxQdCQdmIjBBBAAIEaCdjrhkOHDgkB0BpRsnOUBXTuAF0qmnchytnhcAiEJaCTKOfn55tXYmJiWPuyMQKxFNAObBpv0OsGAqCxrInQj+3pI/B62AkTJpjHtbV3Y2WLjg06efLkok2GDh1a9DM/IIAAAggggAACCCCAAAIIIIAAAggggAACXgh42gNUMzRt2jT5xz/+ISNHjjQzldtu7FlZWeaxIp3RXGdEt4++6z6XXHKJXH/99fojCwIIIIAAAggggAACCCCAAAIIIIAAAr4VsLEu32aQjJUR8DwAqo3ghRdekIkTJ8qUKVPMAfXR+Iq6BOuM8a+88oroNiwIeCnQrl07M55MixYtvEyWtBCIuEDTpk2lZcuWkpqaGvFjcQAEEEAAga8E9LpBb9jr+ZcFAZcEkpKSTHZ1eC0WBFwT6Nixo/nOVq9ePdeyTn7jXCAlJUV06DKdtJrFDYGIRB11dvN3333XvO644w754osvymj07NlTfv7zn8t3vvMdqV3b8yfxyxyPN+JPQE9I+mJBwDUBHf9o+PDhrmWb/CKAAAJOC+hknvpiQcA1gYSEBJNlAqCu1Rz5VYHevXsDgYCTAhr4JPjpVtVFJABqCexs53o3XWfH2rlzp5nhvEePHlxgWiTWCCCAAAIIIIAAAggggAACCCCAAAIIIBAxgYgGQG2utUfo4MGDzUvfq2qCJLsfawQQQAABBBBAAAEEEPBOQJ8wKD4Wf1Upn3POObJy5UphrLOqpPgcAQQQQAABBPwsEJFnz7du3So333yzzJgxQ06fPl2m/CtWrJDrrrtO9uzZU+Yz3kAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLwS8DwAumvXLrngggvkySeflF/96leyZMmSMnkdMWKE/OxnPxN9RP6ZZ54p8zlvIIAAAggggAACCCCAgPcCS5culTNnzpR4zZo1yxxo5syZJd7X7VatWkXvT++rgRQRQAABBBBAIMoCnj8Cf/fdd8vevXtNMXQWzV69epVbpL59+8rTTz8tI0eONAPHjhs3rtzteBOB6groRbsOt9CgQYPqJsF+CMRMQNuuzobJJHExqwIOXImATrQxYMCAcrdYvXq1FBYWSr9+/aROnTplttFhcVgQ8KMA1w1+rBXyFI6AtmEWBFwTOHXqlLluYBZ412qO/KpATk4O8QaHmoKnAdC8vDz505/+JN26dZOf/OQnZob3Jk2aVMihPUF1HKLJkydL//79pVOnThVuywcIhCugE29t3rxZzjvvPGZnCxeP7WMqkJmZaXrPd+zYUXTsNRYE/CaQlpYmn3/+ebnZSkpKkpMnT8oHH3wgTZs2LXcb3kTAjwJ6zaDXDkOHDpU2bdr4MYvkCYFyBfScq8uJEyfK/Zw3EfCzgPZKz87OltGjR5d749TPeSdv8S2gQz+uX79ezj33XDPZd3xruFF6Tx+B37Fjh/mys27dOpk6dapUFvy0PF/72tdEv+y//vrr9i3WCHgioHdjdLFrTxIlEQSiIGDbrF1H4ZAcAgHPBGwPpPLGAPfsICSEQAQE7DnXriNwCJJEICIC+fn5Jl3OuxHhJdEIC2gAv6CgQLQnKAsCLgnYm09cN7hTa54GQOvWrWt6KyUmJoYskJ6ebrb95JNPQt6HDRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVAEPA2Adu3aVTIyMkI5rtlGxwhbtmyZ+Vm7vbMg4KWAHTuxvDHovDwOaSHgtYBtu3btdfqkh0A0BGi/0VDmGF4K2DZr116mTVoIRFLAttlatWpF8jCkjUBEBGz7teuIHIREEYiAgG2zdh2BQ5CkxwKejgGqf3Tbtm0rf/3rX+Wqq66qMqu33nqrHDp0yGyn4yawIOClQI8ePSQ5OVlSUlK8TJa0EIi4gE4gpxPF6ZoFAdcE7E0nu3Yt/+Q3fgW6d+8uOoZtampq/CJQcicF7ORy2n5ZEHBNYODAgaJziSQkJLiWdfIb5wI6941OuKxj47O4IeBpAFSLfN9995kBjPVx+CuvvLJchSNHjsg999wjTz75pPlcvyR961vfKndb3kSgugINGzaULl26VHd39kMgZgJ6M6lz584xOz4HRqAmAtwFr4ke+8ZSQL/E6NNMLAi4JmB7fur3LxYEXBPghr9rNUZ+rUD9+vW5brAYjqw9/yupM2fefffdMn78eBk0aJBMnDjRfJFv0aKFbN++XdasWSMvvPCCmfjIGs2cOdNsa39njQACCCCAAAIIIIAAAggggAACCCCAAAIIeCHgeQBUMzV9+nTRWQjvvfdemTFjRqX5vOGGG0zAtNKN+BABBBBAAAEEEIihgD698t577xUN3aPD/UyaNEl69eoVw1xxaAQQQAABBBBAAAEEEPj/7J0HnBRF1sAfkpeoBMk5CQhI9EQQE2DAiPEMKOZ0CigqqKdiVkRFxdNDPIynGOAUBASVoIKiEpQoQWDJyJLz973yam92md2Z2e2Z7er+1+831Ex3ddV7/y56q19VvRcPAU+DIEU2eOedd8r8+fPlsssuM35BI8/pFiNdKfrll1/K8OHDhe0akXT4DgEIQAACEICAXwj8/vvvcsUVV8iRRx4pF198saxbt86IppO8TZs2FfVdNnHiRL+IixwQgAAEIAABCEAAAhCAQBQCSTOAalvqDHbkyJGyevVq0ZUT33zzjdkGv23bNvP9hBNOiCIShyAAAQhAAAIQgEDBE5g0aZK0atXKjGXUt2n37t1FXfpoOuuss6RSpUoya9YsOfXUU2XAgAEFLzASQAACEIAABCAAAQhAAAJRCSRlC3y0lg4//HCz6jPaub1798pXX30lp5xySrTTHINAngjs2LFD0tPTpU6dOkI04jwh5KICInDw4EFZunSpMa7YyK4FJArN5oOA/m2bPn267Nu3L7MWfS5p0mNffPFF5nH9oo7UjzvuOLHBLLKcdOjHgQMHHJI2Z1HVsNmjRw/Re6bGzhdeeEFq1aolzZo1k40bN8pjjz1mHN8PGTJEBg4cKI8++qi5h/fdd1/OlXLG1wR27twpq1atMuMGdif5+lYhXDYCOm7QFPn3JlsRfkLAtwTWr18ve/bskerVq/tWRgSDQDQCjBuiUfH3sZQZQHPDMH78eJk5cyYG0NwgcS5hAgsXLpTly5dL8eLFzWrkhCvgAggUEIENGzbInDlzpEqVKtKhQ4cCkoJm80vg8ccfl/vvvz9qNTpgijbp99prr0nv3r2jXuPKQfUBrsnmrsgdKacaES699FJj/LzmmmvkH//4R1TDtP59UV/nzZs3l7PPPtv4ND/99NPNtvjI+vjuBoFFixaZyadixYoZY7cbUiMlBCQzuOz27dvBAQHnCOiE465du6Ry5cpStGhR5+RH4PASWLJkiehHJ0110RXJ/wSSYgDV1R+63f3XX3+VBQsWiL7o5ZS0zNSpU0UjwZMg4CUBuwrJ5l7WTV0QSCYB22dtnsy2qDt5BLp27WpWgOpK0MikRhYd4GcfKOkK0OOPPz6yqNPfXe6/b731lhm/NG7cWF566aWoxs/Im3PGGWdIv3795IknnjBG0P/85z+Rp/nuCAHbZ23uiNiICQGxK0BtDhIIuETAPnNt7pLsyBpuArbP2jzcNNzQ3nMDqC5h11UTBARwowMgJQQgAAEIJIeArt4dO3Zsciqn1qQS0Ajvmu666664V6PoStDBgweL7mr5448/pHz58kmVkcohAAEIQAACEIAABCAAgfgJeB4ESaO+Y/yM/wZQMnkE0tLSTOU2T15L1AwBbwmULFnSVEjf9ZYrtaWGgPVh6rLvZXXLo0mDHsWb1Nd5+/btRVf8/vzzz/FeRjkfEbDPXJv7SDREgUCuBNQdhyaXn7u5KsjJQBPQZ67ujMH3cqBvcyCVs+MFmwdSyYAp5ekKUF3xoBFTNV100UVyzjnnGP91OQXxUB9bGiFegweQIOA1Ad26qFtMdVspCQIuEShbtqwxvKgfOhIEXCNgX8A1arqLSbcxqR9elb9q1aoJqWADOKxduzah6yjsDwINGzY0vj8ZN/jjfiBF/ATsxGnp0qXjv4iSEPAJgY4dOxo3Dnb84BOxEAMCMQk0aNDAxBph3BATlW8KeGoA1eht+uKgKyDefffduJTUIB8aSfWjjz6KqzyFIBAvAV2FxMMoXlqU8xsBu5rDb3IhDwRiEbArQGOV8+t5NXyqEWHr1q2ybds2yWkSN5r8GRkZ5rBOYpDcI8C4wb17hsRZCbj+/M2qDb/CQoCVn2G508HUE3uDW/fV0+UZGrlNjZ/169dPiELLli2lT58+CV1DYQhAAAIQgAAEIJAMAo0aNTLVakDHeJNGvZ8xY4YprjsQSBCAAAQgAAEIQAACEICAfwh4agBVtXTr+4QJE0S3tyeSFi9enEhxykIAAhCAAAQgAIGkEOjRo4ep9+WXX467/vfee082bdokzZo1k7p168Z9HQUhAAEIQAACEIAABCAAgeQT8NwAeuutt0rz5s1l+PDhcUs/d+5cGT16dNzlKQgBCEAAAhCAAASSReDGG2802+A//vhjsRHhc2tL/Zn369fPFLnzzjtzK8o5CEAAAhCAAAQgAAEIQKAACHjuA3TRokXy8MMPy8UXX2xWgeq2+NzS8uXLZeTIkSZgUm7lOAcBCEAAAhCAAARSQUDHLk888YTcfPPNcsUVV8iOHTukV69eUZv+9ddfzRgmPT1dTjrpJLn88sujluMgBCAAAQhAAAIQgAAEIFBwBDw1gO7cuVNat24tGgxJk744xJs0YjwJAl4S0JdRNci3adNGSpUq5WXV1AWBpBLYvXu3zJw500QjrlWrVlLbonIIeE1AfWEGId10002ybNkyeeqpp+Sqq66S1157zRhBdayjadKkSebcW2+9JXv37jXjn/fff99Ejw+C/mHUYe3atbJgwQJzL4mmHcYe4K7O+gzSZJ9P7mqC5GEkMG/ePNm+fbu0a9dOCOQVxh7grs7r1q2T+fPnS6tWrYQAmG7cR0+3wJcrV05OPfVUNzRHysATUAPo5s2bZePGjYHXFQWDReCPP/4w/XbVqlXBUgxtQkHgwIEDRk/7Qu6y0k8++aS88cYboitCp02bJtdee60sXbrUqKQuf0aMGCEHDx4U3TI/ZcoUOeKII1xWN/Sy23HDhg0bQs8CAG4RUOORJp1AJUHANQIrVqwQff7aRVSuyY+84SWwZs0aY29g3OBOH/B0Baiqff7558unn34qPXv2NBHhS5YsmSMNfWnQzvLmm2/mWIYTEIAABCAAAQhAoKAI6Bb48847T3R159ixY2XMmDGya9cuOe644+T00083wR8bNGhQUOLRLgQgAAEIQAACEIAABCAQBwHPDaBnn3221KtXT95++20pWrRoHCKIdOrUSaZOnRpXWQpBAAIQgAAEIACBVBLQ7dC6DV4/GuX9l19+kVdffVWaNm2aSjFoCwIQgAAEIAABCEDAJwRw2eCTG5GAGJ4bQHX719ChQ+M2fqqsGjSgfPnyCYhNUQjEJlCtWjXjT6ZChQqxC1MCAj4ioM/DihUrSvXq1X0kFaJAID4Chx32p3edeCdB46uVUhBIPgEdN2zdutU8f5PfGi1AwDsC1td9iRIlvKuUmiCQIgK1a9c272zFihVLUYs0AwFvCFSpUkXUdVmlSpW8qZBakk7AcwOoSnzaaaflKrgG92jcuHGmo1h9WWrbtm2u13ASAokS0AeSfkgQcI1A8eLFpWPHjq6JjbwQMAQKFy4MCQg4SUB9veqHBAHXCNgJJwygrt055FUC7KagH7hKQA2fGD/dunueBkGKVH3Lli3yxBNPyF//+le56667Ik+ZCIXHHHOMPP3001mO8wMCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgJcEkrIC9IsvvpBevXrJypUrjazqFzQyde7c2QQSOOOMM+THH3+U4cOHi654IkEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ8JKA5wbQFStWyDnnnCPbtm3LVc5GjRrJV199JRo59cgjj5TBgwfnWp6TEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQSJeD5Fvg+ffoY42daWpp07dpVLrzwwhxlUmfzukX+ueeekxkzZuRYjhMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJCwHMD6MSJE6V169Zm+/vnn38ul156aa5yNW/eXA4cOCBvv/12ruU4CYFECRw8eND4m030OspDwA8Edu3aZZ6NfpAFGSCQCAF99pIg4CIBxg0u3jVkjiTA8zeSBt9dIbBv3z7Zs2ePK+IiJwSyENi5c2eW3/zwNwFPDaCrV68WDX40dOhQOfzww+PSfPPmzabc7Nmz4ypPIQjES2D+/Pkyfvx4Wb9+fbyXUA4CviCQkZEhOoHEc9EXtwMhEiSwf/9+c4VObpIg4BKBhQsXmnHD2rVrXRIbWSEgO3bsMBRiuSADFQT8SGDatGmii6js+MGPMiITBKIRWLx4sRk3pKenRzvNMR8S8NQAWrlyZSlZsqS0aNEiblWnT59uyv7xxx9xX0NBCMRDwM7G2DyeaygDAT8QsH3W5n6QCRkgEC8BuwKJF5l4iVHOLwTsM9fmfpELOSAQi4BdPcdzNxYpzvuRgBrw9+7dK7oSlAQBlwjYySfGDe7cNU8NoEWKFJFjjjlG5s6dGxeBjz/+WCZMmGDKtm3bNq5rKAQBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAIF4CnhpAtdGePXvK3//+d1H/dbmlTz75RHr16pVZ5Nhjj838zhcIeEHgsMP+7N6FCxf2ojrqgEDKCNi+a/OUNUxDEPCQAP3XQ5hUlRICts/aPCWN0ggEPCBg+2yhQoU8qI0qIJBaArb/2jy1rdMaBPJOwPZZm+e9Jq5MFYEiXjd0++23y5QpU6Rz585yww03iP1DvHXrVlmwYIH8+uuvMmzYMLFb37X9U045Ra644gqvRaG+kBNo1KiRlC1bVqpUqRJyEqjvGoGKFSsaVyKakyDgGgE76WRz1+RH3vASaNiwoZQqVUqqV68eXgho7iSBMmXKGLm1/5Ig4BqBNm3ayO7du6Vo0aKuiY68ISfQoEED4wKyRo0aISfhjvqeG0DV4PnGG2/IBRdcIL179zYkdGu8GqKiJY0Y/8EHH4iWIUHASwJpaWlSr149L6ukLgikhIA+R+vWrZuStmgEAl4TYBbca6LUlyoC6se+fv36qWqOdiDgGQG74IT3Kc+QUlEKCTDhn0LYNOUpgRIlSjBu8JRo8ivzfAu8iqyzkOPGjZOxY8dKy5Ytozo0bty4sbz55psyc+ZMKVeuXPI1pQUIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgdASSuuyye/fuoh/d/j5//nxZtmyZVK1aVXRrskaMJ0EAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCaBpBpAreC6IrRdu3bmY4/ZfPz48VKnTh1jFLXHyCEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIeEEgKVvgExHsl19+MVvhE7mGshCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE4iGQ8ArQRYsWyT//+U8TpU0jtWmUVw14YIMeqBNu64g7+3cVyB47ePCgrFixQv7973+biJsPPfRQPPJSBgJxE9ixY4ekp6ebFcZEI44bGwV9QECfj0uXLpVKlSoZn8o+EAkRIBA3gQMHDsRdloIQ8BOBnTt3yqpVq8y4gWAyfrozyBKLgI4bNO3bty9WUc5DwHcE1q9fL3v27JHq1av7TjYEgkBuBBg35EbHn+cSNoBWq1ZNnn/+edGb7WWaPn26HHfccV5WSV0hJ7Bw4UJZvny5FC9eXGrUqBFyGqjvEoENGzbInDlzpEqVKtKhQweXREdWCMj+/fsNBZuDBAKuENBJfp18KlasmNSqVcsVsZETApKRkWEobN++HRoQcI7ArFmzZNeuXSZGiC6wIkHAFQJLliwR/eikaZ3/d+tI8j+BhLfAlypVSk499VTPNRsxYoTndVJhuAnYVUg2DzcNtHeJgO2zNndJdmSFgCVA/7UkyF0hYPuszV2RGzkhYFeA2hwiEHCJgH3m2twl2ZE13ARsn7V5uGm4oX3CK0BVrdNOO01+++03GTZs2CFL1XXV0oUXXih33nmnnHjiiVK7du3M7fHZkUyaNEkeeeQRefnll6Vly5bZT/MbAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEC+COTJAHrKKafIsmXLpGPHjoc0fskll8i7774rZ5999iHnsh+4+uqrjU/QG264QX766afsp/kNgXwRSEtLM9fbPF+VcTEEUkigZMmSpjX6bgqh05RnBKwfcHwve4Y0aRWNHz9e1q5dm6X+LVu2GB/tRx99dJbjel87d+4c6K3h9plr8ywA+AEBHxNQd0+aeO76+CYhWo4E9Jmrq5fxvZwjIk74lIAdL9jcp2IiVgSBPBlAGzRoIL169Yqo5s+v6n9GnW/HY/y0F/fs2VNuvPFGmThxopx33nn2MDkE8k2gcePGxhdHiRIl8l0XFUAglQTKli0r3bt3N37oUtkubUHACwL2BdwGR/SiTurwnsDPP/8s3bp1S6jik08+2YzXErrIocINGzY0Bl7GDQ7dNEQ1BOzEaenSpSECAecI6KIqNYDa8YNzCiBwaAmoXUxjjTBucKcL5MkAquo1adLkEC016IwGSUokqb+EvXv3YgBNBBpl4yKgq1V4GMWFikI+JGBXc/hQNESCQK4E7ArQXAtxssAJHHXUUdK3b19Zt25dFll0Qjo9PV3U2Bk5ptP7qi6OgpwYNwT57oZDN56/4bjPQdOSlZ9Bu6Ph0gd7g1v3O88G0GhqNmvWTH744Ydop3I8NmXKFFEjqEbeJEEAAhCAAAQgAAEIJJ+ARjp/+umnD2lIV4WqAfSuu+6Srl27HnKeAxCAAAQgAAEIQAACEHCRQMJR4HNTUrdfVK9e3QQ2yq2cPbdkyRK56aabzM/WrVvbw+QQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDwh4KkBVCUaOHCg3H///cavpxo4o6WtW7eayO+dOnWS33//3RRRx/okCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJeEvB0C7wK1qNHD3nsscekf//+8sorr0j79u2lXr16xo/Upk2bZPny5TJz5kxRI6hNGlDpjDPOsD/JIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQh4QsBzA6hKpX6jNIDHgw8+KN9995355CStGkyHDRuW02mOQyDPBNSHmfqWbdOmjZQqVSrP9XAhBFJNYPfu3WaiqFatWiYicarbpz0I5IfA/v3783M510KgwAisXbtWFixYIOqWiWjaBXYbaDgPBDSgrKadO3fm4WougUDBEpg3b55s375d2rVrJwTyKth7QeuJEdBAkvPnz5dWrVpJ2bJlE7uY0gVCwPMt8FaLv/3tb6Jb4DXCaOPGjSUyups63u/QoYOMGzdORo8ebYyl9jpyCHhFQA2gmzdvlo0bN3pVJfVAICUE/vjjD9NvV61alZL2aAQCXhLQwIaa7Au5l3VTFwSSScCOGzZs2JDMZqgbAp4TUOORJp1AJUHANQIrVqwwwff27NnjmujIG3ICa9asMfYGxg3udISkrAC16h9++OEmwqhGGdUXITWIHnbYYVK/fn0pXLiwLUYOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCApBJJqAI2UuGjRotKkSZPIQ1m+66x71apVsxzjBwQgAAEIQAACEIAABCAAAQhAAAIQgAAE/EQAlw1+uhvxyZK0LfDxNf9nqS1btsg//vGPRC6hLARiEqhWrZocccQRUqFChZhlKQABPxEoX768VKxYUapXr+4nsZAFAnER0J0emnTikwQBlwjYcYM+f0kQcImA9XVfokQJl8RGVggYArVr1zYBk9VNHgkCLhGoUqWKsTdUqlTJJbFDLWvKVoDmRPngwYPG+Kl52NKOHTvks88+k8WLF8vKlStF/f1lZGQYg50OvuvUqSNdunQxQXxwGZB479AHkn5IEHCNgAaR69ixo2tiIy8EDAH+XtERXCVQuXJl0Q8JAq4RsBNOGEBdu3PIqwSaNm0KCAg4SUANnxg/3bp1eTKA3n///fLll1/KfffdJ6eeemqmxhq4Q1/a440Aq35B1WGsGv0eeOCBzHqC/mXSpEkyYsQI+eijj2Tbtm0x1dWIYj179pT+/ftLo0aNYpanAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIPAngYQNoLNnz5aHH37YXK0R3vW3TbptU7cPTZw40R4ijyCwbt06ufHGG+XDDz+MOBr7qxqIhw8fboym119/vTz//PNSpEjCty52Q5SAAAQgAAEIQAACEIAABCAAAQhAAAIQgEDACCRsRatbt66UK1dO1G9nq1atDsFxxRVXGANoyZIljTE0t60YugJ0/fr1snnz5kPqCdoBXenauXNnWbBggVGtVq1acvTRR0vDhg2lZs2akpaWZj7Ka+fOnbJ161bzUeOnXjNjxgyzRf7ll1+WJUuWyJgxYwQ/KUHrJegDAQhAAAIQgAAEIAABCEAAAhCAAAQg4DWBhA2gZcqUkd9++01++uknOeGEEw6R5+yzzzb+k2bNmhV3AI+nnnpKtm/ffkhdQTrQu3dv4+vzlltukWuvvVZatGiRsHqrV6+Wxx9/XF588UUZOnSo9OnTJ+E6uAACEIAABCAAAQhAAAIQgAAEIAABCEAAAmEikKco8BpZW6MTRwtcpP4qe/XqFbfxU2FfddVVgWb+66+/yueffy6TJ0+WF154IU/GTwWk7gV0+/vYsWNl0KBBcuDAgUBzy69y2j91NS0JAi4S2LVrF//HXbxxyBx1bAAWCLhAgHGDC3cJGXMjEO3dLLfynIOAHwjs27dP9uzZ4wdRkAECCRPA3pAwsgK9IE8G0HvvvVeaNGmSJQBSpBa33npr5M+Y3zXiuQb5CWr67rvvRH13durUyRMVu3btKsps6dKlntQX1Ermz58v48ePN24WgqojegWTgLq+0EmTSB/LwdQUrYJIwAZCZJIuiHc32DotXLjQjBvWrl0bbEXRLnAEduzYYXSKJ7hq4JRHIecJTJs2zbjQs+MH5xVCgdAQWLx4sRk3pKenh0Zn1xVN2ACqM4svvfSS0XvTpk1R9a9Ro0bU47kdbN68eW6nnT6nwY+aNWvmqQ4dOnSQlStXelpn0CqzszE2D5p+6BNcArbP2jy4mqJZEAnYFUi8yATx7gZbJ/vMtXmwtUW7IBGwq+d47gbproZHFzXga2wQXQlKgoBLBOzkE+MGd+5awgZQ3c6tAZB0NeO4ceMO0bR9+/ayfPnyQ46H+YAahH/44QfPEOjLpfpgbdmypWd1UhEEIAABCEAAAhCAAAQgAAEIQAACEIAABIJIIGEDqEYgP+aYY+SLL76QqlWrHsLk999/NzM4h5zI5YDO9jz55JO5lHD7lK7WfP311+Xjjz/2RJEHH3xQihYtKuXLl/ekvqBWcthhf3bvwoULB1VF9AooAdt3bR5QNVEr4ATovwG/wQFUz/ZZmwdQRVQKKAHbZwsVKhRQDVEryARs/7V5kHVFt2ARsH3W5sHSLpjaJBwFvlatWlK3bl1jgPMKifpOsMuHvarTT/XUr19fzjrrLDn33HPlggsuMP5ATzjhBClSJH78uqVl0qRJ8vTTTxs/Ey+//LKfVPSlLI0aNRINylWlShVfyodQEMiJgPr4bdGihfH1m1MZjkPArwTspJPN/SonckEgO4GGDRtKqVKlEgrkmb0OfkOgIAiUKVPGNKv9lwQB1wi0adNGdu/e7al9wTUGyOsmgQYNGkjJkiUlLy4g3dTYfanjt8D9V1d9KV+2bJlZ0dijR498vaDrys/Vq1fL3//+dxNUyX2cOWswbNgwmTt3rrz//vvmU65cOeMXVP/T1KlTxwy409LSzH8g5bJ9+3ZjFF61apWo24E5c+aI9bk6YMAAueGGG3JujDOGgPKsV68eNCDgHAFdwaETTSQIuEiAWXAX7xoyKwF9idFJaxIEXCNgV34msrjCNR2RN7gEdOKfBAEXCZQoUYJxg2M3LmEDqP6BHTx4sJx22mly9dVXm8Fi5MuOruTUCPHxJI0Qa4MlPPDAA/Fc4mwZfbBPnjzZrP4cM2aM8aM6ffp00U+8SQfmffr0kUGDBsV7CeUgAAEIQAACEIAABCAAAQhAAAIQgAAEIBBqAgn7AFVaun37q6++kuOOO0404pWuVrQfNWjqdu14Ptb4GZY7oD5TR48eLWPHjpVLL71U7HaVWPpXqlTJrJLV4FIYP2PR4jwEIAABCEAAAhCAAAQgAAEIQAACEIAABP5HIOEVoPbSdu3aybRp02Tt2rVmi/Yvv/xi8tdee026dOlitnTbsjnl6utDjXq6vTtMqXv37qKfXbt2ycSJR6xAdgAAQABJREFUE2XRokWSnp4ua9askYyMDBNcSn2t6qdmzZqirHX1JwkCEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIjECeDaC2mSOPPFL0o0ZPTR988IG88MILor4t401Dhw6VDRs2xFs8MOXUZ8SZZ54ZGH1QBAIQgAAEIAABCEAAAhCAAAQgAAEIQAACfiOQpy3wXitx+eWXi3Xe7XXdQavv2WefNUGTCIIU+86qP9olS5YYdwyxS1MCAv4hoO5BfvvtN9m6dat/hEISCMRJQP17kyDgIgF167R48WLRYJQkCLhEwLoVo++6dNeQ1RJYv369aOBfEgRcI8C4wbU7JlLo//9gHvRS7E8++UROPvlkKV26dELVaoT05s2bJ3RN2Ar/8ccfZrXtnj17jOpTpkyR448/PukYXn31VXnxxRczA1blp8GFCxearf+6+rVatWqiAbR0e380A7i6CIgcyBUrVkz0kz1pF1ZjZ2RX1jqVk370u71O69N6bUpm+4ULF7bNZOa0D/94+p/2E/WrrP8vbJC5RPs//Y//f5kPnv9+SdXzZ+nSpeZ53LRpU4mMSJyq9vWZn8z+v2DBAlEXPo0aNZLixYtH/fuTzPYj72tOf//y075Ovmzbtk2qVKkiaWlpprmwPH/0RUbHDUWLFpVIQ34Y9FeXVvpRv/OHH354vsZf+el/2uHyO/6L1r7uNFu9erV5P6lcuXJSx5/R2k/280/HwepKS8fX+mzKnpLdvm0v2c9f247myXj+ab3J6H+x+OvzRt+FddzXsGHDwP3/i6W/Tvgrg7Jly5q/r1repjA8f62umhdE/6P9/xFIlL/GvdFnkb6v6Zgpmc//FStWiNqDRo4cKZdddtn/hOZbQgQ8N4Am1Pp/C+/du9cEVTrllFPycnlortE/DOpaQF8udXCu/lM1sFKyk/4He+utt5LdDPVDAAIQgAAEIAABCEAAAhCAAAQgAAEIRCGAATQKlAQO5dsHaAJt5Vh0/PjxMnPmTMEAmiMic0JnFr766it57733DKtUGD+14eHDh8vdd9+dZTVE7pLmfPaiiy6S+fPnS6dOneSxxx4zq2d0Zi9a0tk/XY2hSWdEdVbFrobLXl5na9SQrklnXrTsr7/+ambDmzRpksVQrCvr7MoOXb2TrPazy2h/0z78Y/W/jRs3yuzZs6VcuXJmJUBe+r/tb9lz+h/9L1b/0z6Tn+fviSeeaFYwfPnll2YlWWQfDEL/O/fcc42Lig8//FDq169vdM3+9ydS58jvLuivLna++eYbGTx4sLRv3z5Pf38jdY787nf9dXyiQSl1BZY+fzWF5fmrQUzVh3/v3r3lpptuytf4K/KeR34vyPs/evRoue+++6Rbt24mD9r4T3eF3XPPPaJjXn1PiJYKkr/KQ/s5jz/0PaZDhw7mvWj69OmB+/8X6/5PnTrVvMd17NjRvOvl5/0vWt+P1X7kNfkZ/9hdE5H12e/0/5z7v2WkuWv81d3eunXrpHbt2lKvXr1IVbJ89+L+33vvvfLpp59mqZcfiRNIigFUX6508KwGKN0qZh9i0cTTMvrQ0z/apNgENCp8v379Yhf0sIQaCL1yT6BbYzRVqFBB9I9cMpPKrVuB2rRpIxUrVkxmU9QNAU8JZGRkiH7q1KkjLVu29LRuKoNAsgnYiSrtu+XLl092cymvX/+uaGrcuLHoNv+gpTJlyhiVmjVrlvS/035jp/dWDWNt27YV3SYdpqQuDzSp3q1btw6c6j/99FOmfskefxYEvC1btphm1QVZixYtCkIE2swHAfuurH8/g/j/LxaazZs3GwP5McccE3ULcazrOQ+BgiJwxBFHyLx588wEhv07mixZ1D0NKf8EPDeAqhPjSy+9VCZOnJh/6agBAvkgoC+nakCyL6v5qIpLIZBSAuoDqXv37jmuTk6pMDQGgQQJWP9H1hCa4OUUh0CBEdCVn7Vq1WLcUGB3gIbzSsAuMEg0BkNe2+M6CHhJQCcl1I+iHT94WTd1QSCZBNQ9YY0aNRg3JBOyx3V7bgBVf5EYPz2+S1SXJwK6bQ3jZ57QcZEPCOgqJBIEXCSgz14SBFwkwLjBxbuGzJEEeP5G0uC7KwQiAya6IjNyQsASwN5gSbiRe2oA1ahUkyZNMpqrr8dzzjnHLAe226myI1EfDxqRceDAgdlP8RsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQL4JeGoA3bNnjwkuo07z33333biEU18JGgzno48+iqt8kArt2LFDPvvsM1m8eLGsXLlSVq1aZfz+qX9M9Vmp27e7dOlifFiyJSBIdx5dIAABCEAAAhCAAAQgAAEIQAACEIAABFJFwFMDqDpOV+Nn3bp1E5JfAyUkek1CDfissK6SHTFihDH6btu2LaZ06g+wZ8+e0r9/f2nUqFHM8hSAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE/iRwmNcgdOv7hAkTRLe3J5J0FWTQ07p16+T888+Xk08+WUaOHCnxGD+ViUaDHj58uBx11FFy0003Jcw26FzRDwIQgAAEIAABCEAAAhCAAAQgAAEIQAACORHwdAWoNnLrrbfKJ598Ygx21113XU7tZjk+d+5cGT16tLRu3TrL8SD92LBhg3Tu3FkWLFhg1NIoo0cffbRoxNGaNWtKWlqa+agT3Z07d8rWrVvNR42fes2MGTPMFvmXX35ZlixZImPGjCFCdIwOkp6eLosWLTIuBEqVKhWjNKch4B8Cu3fvlpkzZ5poxPqsIEHAJQL79+93SVxkhUAmgbVr15oxl45HiaadiYUvDhDYu3evkVLfIUgQcI3AvHnzZPv27dKuXTshkJdrdy/c8uoCt/nz50urVq1Ed+2S/E/AUwOo+gBVg9PDDz8sF198sVmpqNvic0vLly83qyE1YFKQU+/evY2vz1tuuUWuvfZaadGiRcLqasCoxx9/XF588UUZOnSo9OnTJ+E6wnSBGkA3b94sGzduFAygYbrz7uuqAeW036rvXwyg7t/PsGlw4MABo7J9IQ+b/ujrLgE7btBJawyg7t7HMEquxiNNOoFKgoBrBFasWCFqR9BP8eLFXRMfeUNMYM2aNcbeoOMGDKBudARPDaA666iz5vrw0nTzzTfHTSHIBtBff/1VPv/8c5k8ebJ06tQpbibZC1arVk2ef/55OfPMM42B+fbbb5fDDvPci0H2ZvkNAQhAAAIQgAAEIAABCEAAAhCAAAQgAAFnCXhqPStXrpyceuqpzsJIluDfffedXH/99fkyfkbK1rVrVxMlfunSpZGH+Q4BCEAAAhCAAAQgAAEIQAACEIAABCCQZAK4bEgy4CRU7+kKUJVPg/x8+umnJmq5RoQvWbJkjmIfPHhQdLnwm2++mWOZIJxQ3xDNmjXzVJUOHTrIypUrpX79+p7WG6TKdMWsbgmqUKFCkNRClxAQKF++vJnkqF69egi0RcWgEbA7E4oWLRo01dAn4AR03KA+2CtWrBhwTVEvaASsqyeNJUCCgGsEateubd7ZihUr5proyBtyAlWqVBF1XVapUqWQk3BHfc8NoGeffbbUq1dP3n77bYn35Ue3hU+dOtUdaglKWqNGDfnqq68SvCrn4mo4/umnn6Rly5Y5F+KM6ANJPyQIuEZA/R917NjRNbGRFwKGgPquJUHARQLqtz6W73oX9ULm4BOw71wYQIN/r4OoYdOmTYOoFjqFgIAaPjF+unWjPd0Cr6ofccQRJkCP/UMcD46TTjrJ+LWMp6yLZXS15uuvvy4ff/yxJ+I/+OCDxrisq8RIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQM4EPDeAalOnnXZa1BZ15WK0pNvl2rZtG+1UII7pNvWzzjpLzj33XLnwwgvliy++kH379iWk2/79+2XChAnSrVs3UQPoddddl9D1FIYABCAAAQhAAAIQgAAEIAABCEAAAhCAQBgJeL4FPhLi6tWr5ZlnnpHZs2fLvHnzZOPGjdKoUSNp3ry56Fb5iy++OLJ4oL8PGzZM5s6dK++//775aMAo9QvaoEEDqVOnjqjvnrS0NOMzVY2j6rtyx44dsmrVKtEo8nPmzJFNmzYZRgMGDJAbbrgh0LxQDgIQgAAEIAABCEAAAhCAQFgIJLpAJixc0BMCEICAVwSSYgDVlZ6vvvqq3HXXXbJly5YssqoRUD/vvvuuvPTSS/LCCy+EwpelOtSfPHmyiQY/ZswYw2X69Omin3iTBpTq06ePDBo0KN5LKAcBCEAAAhCAAAQgAAEIQAACPiUwbtw4I9nevXvlu+++E3WfRoIABCAAAe8JJGULvK76vP766w8xfuoKRw1KYwMkTJkyxQT60BWOYUhVq1aV0aNHy9ixY+XSSy+VMmXKxKW2Otb9+9//LsuXL8f4GRexPwupIX7nzp0JXEFRCPiHwK5du+TAgQP+EQhJIBAngZzc3cR5OcUgUGAEwjpuyMjIkPnz5xvuCxcuNBFtC+wm0HC+CPD8zRe+ArlYjZ79+/fPbPtvf/ubhO0+6srXPXv2ZDLgCwRcIoC9waW7JeL5CtAFCxbIfffdZygcfvjhctFFF8kll1xitntXqFDBHFd/lrq1e9SoUaJbw3v27CkzZsww28Ddwpc3abt37y76UQPHxIkTZdGiRZKeni5r1qwRHYSqobRWrVrmU7NmTWnXrp3ZGp+31sJ7lQ7mdSB/3HHHEZ0tvN3ASc31OaArxmvXri2tWrVyUgeEDi8B/RuvCQN+ePuAq5rrmEHHDscee6wceeSRrqoRt9xqeNEx+5AhQ2T37t3mOh2b62T9zTffLI899pgQVTxunAVaUN1madq2bVuBykHjiRMYOnSoeRe0V+oK0DfffFMuv/xyeyjw+bRp04z7N411YRdKBV5pFAwEgcWLFxtXj+3btzc2nEAoFXAlPDeA3n///cawd8UVV8grr7wSdeCkDzY18N1xxx1y7bXXSo8ePeTFF180W+YDzjuLejqoPPPMM7Mc44d3BOxsjM29q5maIJBcArbP2jy5rVE7BLwlYFeuWEOot7VTGwSSR8A+c22evJYKvmZdcaVj0PHjxx8ijBpG1Sj6ww8/mACcxYsXP6QMB/xFwK6e47nrr/sSS5r169fLQw89lFmsaNGiov//7r77bjnvvPNCszhIDfiqtz6XMIBmdge+OEDATj6FYdzgwO2IS0TPt8B///33Zhn/G2+8EdX4mV2q0qVLi8586QpQEgQgAAEIQAACEIAABCCQXAJPPvlkVONnZKvqqurBBx+MPMR3CEDAQwIDBw40Lie6du1qalXjn/r/1EDCjz76qIctURUEIAABCCgBTw2guu1i2bJlcttttyVEt2nTpiYwUkIXURgCMQgcdtif3ZuZxBigOO07Arbv2tx3AiIQBOIgQP+NAxJFfEXA9lmb+0o4D4XRVYKDBw+Oq8bnn38+c3t8XBdQqEAI2D5bqFChAmmfRhMn8PPPP8trr70muupTJyQ06f177rnnTK7/R/W9OgzJ9l+bh0FndAwGAdtnbR4MrYKthadb4HXpb8OGDaVatWoJUdMtNtu3b0/oGgpDIBaBRo0aSdmyZU3grVhlOQ8BPxGoWLGitGjRQjQnQcA1AnbSyeauyW/lVV/lV199tfHNbY9pvnTpUvNT/ZtrcEebdPDbt29fs23RHiN3i4COYUuVKiXVq1d3S/AEpZ03b55s3Lgxrqt0fK7jdPWnTvIvARtYVfsvyQ0Ct99+u/GVrUGP9J3FJl0Betlll8nIkSOlX79+8sEHH9hTgc3btGljJlrUGEyCgEsEGjRoYGK11KhRwyWxQy2rpwZQjVauLwDqwyPeB5j6CtNASK1btw71jUB57wnoi2m9evW8r5gaIZBkAroCoG7dukluheohkBwCQZkF16CO0fwjWmqzZ8+2XzNznbhQv20kNwmULFlS6tev76bwCUi9adOmBEqLbN68OaHyFE49Abvys0gRT1/tUq9ISFpUo+aXX35pJro1fkb29Pjjj8uHH35oAgZruS5dumQvEqjfTPgH6naGShmN6RKGcUOQbqrnfyXV4KTOnB9++OGYnHTF6JVXXinvv/++DBo0KGZ5CkAAAhCAAAQgAIFUEDjppJNMZM+MjIwszemKuK1btx6yu0BXvKoBlAQBvxNIdKVK0FfE+v1+IV+wCOzatUvuvPNOo5S+/5YvX16yB1DR3ZT33nuvDBgwQHSF6KxZswgOFKxugDYQgEABEfDcANq/f38zSzV16lR55plnpGXLloc8sHXrzYgRI+TNN9+UNWvWSJ06deSWW24pIAQ0CwEIQAACEIAABA4loD7KSRAIGgHdsqdbbhcuXBhTNTWW6lieBAEIeENA34/Vt6f+v7r22mtzrLRPnz7y6quviu420PyGG27IsSwnIAABCEAgPgKeBkHSJjt16mRmrHS5vvrzsMuCjz/+eGncuLHxkdC8eXN5+umnjfFTV0yoIbRcuXLxSUwpCEAAAhCAAAQgAAEIQCDPBOLZqaWV664uu706z41xIQQgYAhodPfHHnvMfB8yZIhxHZcTGn2H1vdlTffdd5+JFp9TWY5DAAIQgEB8BDw3gGqzDzzwgNxzzz3GD+i+ffvkt99+k2nTppmZZl32b5Ou/PzPf/4jHTt2tIfIIQABCEAAAhCAAAQgAIEkErjwwgvN9trcmtAgLVdddVVuRTgHAQgkQEB3SqoblZ49e8bl1/P888835TZs2CAPPvhgAi1RFAIQgAAEohFIigFUHXA/+uij8uOPP8q5554rNWvWzGxbHcxrwCP1a6Jb4bt37555ji8Q8JLAjh07ZMmSJbJ//34vq6UuCCSdgAaH04kj9TNIgoBrBA4cOOCayMgLAUNA/fAtXrxYdPI+DEn9D44ePVratm2bRd1WrVoZ//zPPvtsluP88C8BHTdoCkvf9e+dyFmyb7/9Vt566y2zO/Kpp57KuWC2M88995xxJ/fiiy/K/Pnzs50Nxs/169fLqlWrgqEMWoSKQNjGDUG4uUkxgFowzZo1MxHsVqxYYZbtqzFq27Zt8sMPP8gjjzwiGqWbBIFkEVDfVnPnzpX09PRkNUG9EEgKAZ3pnzNnjvzyyy9JqZ9KIZBMAnbSyebJbIu6IeAlgUWLFpnJed2mGpbUo0cPmTlzZuZqUA24ogsYdIUayR0CNlibri4k+Y+AGqj1/5bmffv2NfEv4pVSg+upr9C9e/fKHXfcEe9lTpXTIE/ff/+90dEpwRE29ATUvqWL+lauXBl6Fq4ASKoBNBKC+vjUCPGHHfZnk7o6j1nKSEJ895qAXYVkc6/rpz4IJIuA7bM2T1Y71AuBZBKg/yaTLnUng4DtszZPRht+rdMuSrC5X+VErugE7ApQm0cvxdGCIjBy5EiZMWOGaHR3dROXaFKfvRotfty4cfLZZ58lernvy9tnrs19LzACQuC/BGyftTlg/E8g4Sjwe/bskTvvvNP4L1Ejps40RuaffvqpVK9ePabmGgVeZ3qGDx8esywFIAABCEAAAhCAAAQgAAEIQAACLhHQ3Y933323EfmJJ56QUqVKJSx+xYoVTYwNXQGqn1NPPdXE2ki4Ii6AAAQgEHICCRtAixYtalZx/vOf/8xEV7p0abntttvk6quvjsv4qRdef/31MmrUKBk4cKCoDyISBLwmYFcx2Nzr+qkPAskioL6SNdF3k0WYepNJwEaMLly4cDKboW4IeE7APnNt7nkDVAiBJBEoXry4qZnnbpIA56NajeRu3XHdf//9xpCZvTq7ekyDBdevXz/7afPb7pxUF1/Dhg2TW2+9NWo5Fw/qM1dXL2scERIEXCJgxws2d0n2sMqa8FNGX2wGDBggQ4YMMcw6d+5sHDrXqFEjIYb6B/qdd94xAZHU2Tq+hhLCR+E4CDRu3Nj42ClRokQcpSkCAf8QKFu2rAkQV6xYMf8IhSQQiJOAfQG3Lm/ivIxiEChwAg0bNpRatWqZICUFLgwCQCABAnbiVBelkPxFYN26dZkCLV26NPN7tC9qBNQgmLGSBg0KUurYsaMxgNrxQ5B0Q5dgE2jQoIGoHQx7gzv3OWEDqKpmI9CpT8+PPvpIjjjiiDxpXLlyZdGZMPVrggE0Twi5KBcCaqznYZQLIE75moBdzeFrIREOAlEI2BWgUU5xCAK+JsC4wde3B+HiIMDzNw5IKS6i0dv79+8vuQUG1JWfGjxYx36xAmDqKkmdqAlSYuVnkO5m+HTB3uDWPc+TAXTSpElma+bHH3+cZ+OnxXTppZdKv379jFPn7t2728PkEIAABCAAAQhAAAIQgAAEIAABZwmoUbp27dq5yr9z505zXndO6AIjEgQgAAEIJIdAnqLAz5s3T0488UQ5+uij8y2VbtU45ZRTzDb6fFdGBRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIgjkyQC6aNEiadeuXUQ1+ftat25dUYfOJAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACXhLIswG0TZs2nslRpUoVWbx4sWf1UREEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAASWQsAF03759sn37dtmyZYtnBBcsWCCbN2+WAwcOeFYnFUEgPT1dvv76a9NfoQEBlwjs3r1bpk6dKitWrHBJbGSFgCGQW6AHEEHAzwTWrl1rxg3btm3zs5jIBoFDCOzdu9ccs74kDynAAQj4mIC615sxY4aJBO9jMRENAocQWLdunRk3ZGRkHHKOA/4kkLABVKO0HX744fLtt996ptH3339vgimp42cSBLwioAZQNaxv3LjRqyqpBwIpIfDHH3+Yfrtq1aqUtEcjEPCSgJ3MtC/kXtZNXRBIJgE7btiwYUMym6FuCHhOQBenaNIJVBIEXCOgE/76/N2zZ49roiNvyAmsWbPG2BsYN7jTEfJkcaxRo4ZnBlBdSaqzPlWrVnWHGpJCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACThDIkwH05JNPFl21OXLkyHwred1114luq+/UqVO+66ICCEAAAhCAAAQgAAEIQAACEIAABCAAAQgkk0ChQoWSWT11J4FAngygZ511lhFFjZfqryOv6aWXXpJ///vf5vLTTjstr9VwHQSiEqhWrZpxrVChQoWo5zkIAb8SKF++vFSsWFGqV6/uVxGRCwI5ErDubIoWLZpjGU5AwI8E7LhBn78kCLhEoFSpUkbcEiVKuCQ2skLAEKhdu7bo87dYsWIQgYBTBDSY9xFHHCGVKlVySu4wC1skL8p36dJFjj/+eBOk45xzzpHnn39ezj//fInXAq7+aZ577jm5//77TfNHHXWUnH766XkRhWsgkCMBfSDphwQB1wgUL15cOnbs6JrYyAsBQ6Bw4cKQgICTBCpXriz6IUHArwTUt3I0P5+6m06TxmqIFsRL39GskdSvuiFXeAk0bdo0vMqjudME1PCJ8dOtW5inFaCq4uDBg0VXd6jD4gsuuEBatWolH330Ua6R3PWPs674VINn//79M/+AP/7448ILk1sdB2khAAEIQAACEIAABCAAgdQRGDJkiJQpU+aQzxlnnGGEmDJlyiHntLyuriNBAAIQgAAEwk4gTytAFVq7du3k1VdflV69ehmGs2fPlvPOO88YRWvVqiV169Y1n7S0NFm8eLEsXLhQli5davx9mgv++8/dd98tdkt95HG+QwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDIL4E8G0C14SuvvNJse7/jjjtk06ZNRhbdmrFkyRLzyU043aLRt29fefTRR3MrxjkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAnkmkOct8LbFK664QubPny+XX365PRQzV992s2bNEt36Hq/f0JiVUgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQDYC+VoBautSx6//+te/5KqrrpJRo0bJ3LlzzWfjxo2mSMmSJaVJkybSrFkz6dq1q1x22WUYPi088qQROHjwoOzatUu0/5Eg4BoB7bsaDdNG1HZNfuQNLwF99pIg4CIBxg0u3jVkhgAEXCegcUIOHDhAFHjXb2RI5d+5cyf2BofuvScGUKvviSeeKPqxac2aNbJ9+3bjC5SXeEuFPFUEdGWy+p497rjjiM6WKui04wmBjIwMmTx5stSuXdsEmPOkUiqBQIoI7N+/37SkLzMkCLhEQMcMOnY49thj5cgjj3RJdGSFAAQg4CyBadOmGZtBt27dCIzs7F0Mp+Aa62bevHnSvn17qVq1ajghOKa1pwbQ7LpXqVIl+yF+QyBlBHQ2RpPNU9YwDUEgnwRsn7V5PqvjcgiklIBdAWoNoSltnMYgkA8C9plr83xUxaUQgAAEIBAngR07dojGEdGVoIULF47zKopBoOAJaN/VxLih4O9FvBLk2wdovA1RDgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAqglgAE01cdpLGQHrdoGZxJQhpyGPCNi+a3OPqqUaCKSUAP03pbhpzAMCts/a3IMqqQICEIAABGIQsM9cm8cozmkI+IaA7bM2941gCJIjgaRugc+xVU5AIAUEGjVqJGXLlhVcMaQANk14SqBixYrSokUL0ZwEAdcI2Eknm7smP/KGl0DDhg2lVKlSUr169fBCQHMIQAACKSbQpk0b2b17txQtWjTFLdMcBPJHoEGDBiYAUo0aNfJXEVenjAAG0JShpqFUE0hLS5N69eqlulnag0C+CRQqVMgEj8t3RVQAgQIgwCx4AUCnSU8IlCxZUurXr+9JXVQCAQhAAALxEWDCPz5OlPIfgRIlSjBu8N9tyVUitsDnioeTEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgMsEMIC6fPeQHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMiVAFvgc8XDSQhAAAIQgAAEIAABCEAAAhBIFYENGzbIgQMHsjRnfURmd7OirivKlCmTpSw/IAABCEAAAtEIYACNRoVjEIAABCAAAQhAAAIQgAAEfESgR48eEi3Yxrx58+SRRx6Rpk2bysCBAw+R2KXgMvfdd58MGjToEB1yOqAB97766ivp2LFjTkU4DgEIQAACEDAEMIDSEQJLYMeOHZKeni516tQRohEH9jYHUrGDBw/K0qVLpVKlSqxqCOQdDoZSS5Yskf79+8uuXbuyKLR9+3bz+4ILLsgS0VVfwAcMGCBt27bNUp4fEPALgZ07d8qqVavMuKFIEYbIfrkvyPE/Ak2aNBH9ZE+TJk0yBtAKFSrIJZdckv20U79r1aolVatWlf3792fKvW/fPtm0aZMZz6uOkUlXfx5++OGRh/juGIH169fLnj17pHr16o5JjrhhJ8C4wb0ekPTR3XvvvSdTpkwxhqiGDRtKixYt5PTTT5fy5cu7RwuJnSKwcOFCWb58uRQvXjzqbLlTyiBsqAjo1q85c+ZIlSpVpEOHDqHSHWXdIfDll1/KqFGjchR4/Pjxh5zTMQAG0EOwcMAnBBYtWmQmn4oVKyZqhCFBwBUCGRkZRlQ7AeWK3NHkvPbaa0U/kUn/bzZq1Ejq1asnOr4nBYvArFmzzGRq5cqVs0ycBktLtAkiAV0MoB+dNNVFVyT/E0iqAfSmm26SGTNmyOWXXy5XX321LFu2TH788UezreG8886TO+64Q7LP4vkfGRK6QsD6DrK5K3IjJwRsn7U5RCDgRwJXXXWVGexlXwE6YcIEs5KjW7duZkBoZdfBYZcuXexPcgj4joB95trcdwIiEARyIKA7RzTZPIdiHIaALwnYZ67NfSkkQkEgCgHbZ20epQiHfEYgaQZQ3b45fPhwM5Ou2xg0tW7dWtTwqb5p3njjDeOrZeTIkdKuXTufYUEcCEAAAhCAAARyI6CBKE4++eSoRXS7ou72cMnvXFRFOAgBCASCgG5T1KRbbUkQgAAEIAABCISTwGHJUlu3b6ph0xo/I9vRLcnXXXedfPbZZ3LzzTfL1KlTI0/zHQKeEEhLSzP12NyTSqkEAikgoBFNNdF3UwCbJjwnoP1WtxDje9lztFSYZAL2mWvzJDdH9SkkMHr0aNOa7kwL4ipJfbfSxHPXYOAfxwjoM1cnTPG97NiNQ9zMdzXGDe50hoRXgO7evds4pY51k9VHy7fffmt8f0YzgioiLTN27Fhp06aN/Pbbb6KrSUgQ8IpA48aNzfbMEiVKeFUl9UAgJQTKli0r3bt3N0aklDRIIxDwkECnTp2MgYG/6R5CpaqUEFBf9er7k3FDSnCnrBH1z/b555+b9jSQju5C69WrV8raT0VDduK0dOnSqWiONiDgKYGOHTuacQMGfE+xUlkKCDRo0MDEGmHckALYHjWRsMVxzZo1xgm1Dh5ym0Ft1qyZ6Et87969TdS+nORVH6DHHnusqPNjEgS8JFCoUCFeYrwESl0pJaCrObQPkyDgGgFdwcHWd9fu2v/ktVuF/3ckPN8YNwTzXvft2zdLRPF77rlHtm7dGkhlGTcE8rYGXinGDYG/xYFWEOOnW7c3YQOozoy3atXKzJzqys1JkyZF1Vj/AOs2d13h2bJlS4kWDdZeWK5cuRzrsWXIIQABCEAAAhCAAASSR2D//v2ZE9LqpogEAdcJTJw4UT755JMsauhijkceeSTLMX5AAAIQgAAEIBB8AgkbQNWw+eabb5otQrrdQgMgnHXWWTJ//vxDaA0aNMicX7lypWg02BYtWsjLL78sa9euzSz7888/y/vvvy9lypTJPMYXCEAAAhCAAAQgAIHUEtAx2vbt202jI0aMkC1btqRWAFqDgIcE1KB/++23R61xyJAholvjSRCAAAQgAAEIhIdAwgZQRVO+fHm59dZbzSpQXeGpEd+PPvpoueWWW2TDhg2Z9NSPx3vvvScdOnQwxzQw0k033SRVqlQR3fquvpZ0Nalut1J/dyQIQAACEIAABCAAgdQTUN+IDzzwQGbDavx88MEHM3/zBQKuEVCD/rx586KKrTEN+vXrF/UcByEAAQhAAAIQCCaBPBlAFcWFF15otpSo4fKnn36Sl156SUaNGiXqCPbJJ58UHVhoUkPn9OnTZfDgwZlRsvS4DrQXL15sfDS++OKLUrduXT1MggAEIAABCEAAAhBIMYH777/fjM2OOOII07Lu+Bk6dKgsXLgwxZLQHATyTyC7QT9ajR9//LF88cUX0U5xDAIQgAAEIACBABLIswG0Ro0asn79eoNEV3pee+21smjRIrntttvMigGNwP3OO++Y8xoJ9o477pBly5YZo+l9991nVoKqUXTu3Lly9dVXBxAtKhU0gfT0dPn6668zt/MVtDy0D4F4CegE0tSpU2XFihXxXkI5CPiGgO72+P77730jD4LEJqCr5IYNGyY6nmvSpIm54LTTTpO9e/ea8VvsGoJRQl006bhh27ZtwVAoxFroamY1gsZKukVet8q7nvT/qqYwBzFz/R6GWX79GzRjxoxcAyyHmQ+6+5fAunXrzLghIyPDv0IiWRYCeTaAqlFTfYBGptKlS8tDDz1kVgt06dJFLrvsMrP9fdq0aaZYpUqVjL9QLaOrPtUoWr9+/cgq+A4BzwioAXTz5s2yceNGz+qkIgikgsAff/xh+u2qVatS0RxtQMBTAmq4175rX8g9rZzKkkLAGoFuvPFG0bGcpiuvvFI0SKUGQxo3blxS2vVbpXbcEOnOyW8yIk9sAtagH7ukmIUYavx3PVnfvXYHnuv6IH+4COi4QZ+/e/bsCZfiaOs8AQ2qp/YGxg3u3Mo8G0DVcbgOknWmXGdsImfLq1evLuo8/4cffjBljj/+eOnZsyfOxt3pF0gKAQhAAAIQgEAICGiEbI2UrVvfI31+qr933RavSSesMWiHoDMEREXtr/v27YtbG10tqi+wJAhAAAIQgAAEgk0gzwZQ3cauW9o1yNHzzz9vor1rlPdHHnkk0xiqAY7Ut85//vMf+eWXX6Rp06bSp08fBhnB7lNoBwEIQAACEICAAwR0tU3fvn2NpGr8tP4/rega8LJRo0Yyf/58s3PHHieHgF8JjB49WiZMmJCQeLpTKDIAWEIXUxgCEIAABEJLQP2lk9wikCcD6AcffCAffvih2RalW9nffPNN+e6770ywo6JFi8qxxx4r//jHPzJJnHHGGTJ79mwZMmSIvPXWWyZQkn5nNUEmIr4kgUC1atXMy5wG4iJBwCUCuvKqYsWKoqvpSRBwjUDt2rVF/YTreIDkbwI6FtMdPc2aNZMbbrjhEGH1Hj777LPmuBpIg77Fy44b9PlLco9ApEE/Uek1Yrwu1nA1lSpVyoheokQJV1VA7hAT0HGDPn+LFSsWYgqo7iKBKlWqGHuDunokuUEgTwbQKVOmyC233CK1atXKoqVuib/rrrvk22+/NRHhNciRTUWKFBH1LaWR36+//nq55557zIpQNaSSIJAMAvpA6tSpk9hBYTLaoE4IJINA8eLFpWPHjoc8Y5PRFnVCwGsCzZs3lzZt2nhdLfV5TED9Vg0aNMjUqoZQHadFS6effrp0795d1DfxwIEDoxUJzLHKlSubcYP1gxoYxUKiiPZjfc/IS9It8+oL19VkJ5wwgLp6B8Mtt+4SbdeunbCaLtz9wEXt1fCp9oYyZcq4KH4oZc6TAVQHCWXLls0RmA4cdQvKpEmTZMT/+wKNTNo5Hn30URMoSVeKqm9Q7TQzZ86MLMZ3CEAAAhCAAAQgAIEkEbj33ntl69atcvbZZ8spp5ySayu6ClQNLK+99prZ0ZNrYU5CoAAIrF27NtOgn9fmdev8mDFj8no510EAAhCAAAQg4HMCeTKAdu7c2fiCUh+gOSVdwaQrC5577rmoRWrWrCkjR440hs/ChQvLSSedFLUcByEAAQhAAAIQgAAEvCOgQSp1glq3Gz7zzDMxK27SpIncfPPNsn//fvnb3/4WszwFIJBqAtagn992NVYBkajzS5HrIQABCEAAAv4kEH2/UwxZTzjhBBPIqHXr1vKvf/1LzjzzzKhXaBCk9PR04zMqJ39Kuk3uyy+/lHHjxkWtg4MQgAAEIAABCEAAAt4RUCPmwYMHzZbf+vXrx1WxBolRn+86Zhs1apScf/75cV1HIQikgsAll1wiF110UY5N6erOp59+2gRtVXdduaXt27fjizA3QJyDAAQgAAEIOEogTwZQ9a04bNgw6d27t5x11lnSo0cP6devn9nKHsnh119/Fd2SsmLFChPQI/Jc9u/qX4qUM4EdO3ZIWlpazgU4AwEIQAACEIAABGIQeOedd2TatGly5JFHJuTTU4Oz6c4eDZakYz4NcIm/wRiwOZ0yArHcOKjPW00aaKVr164pk4uGIAABCEAAAhDwD4E8bYFX8a+++mrp27evWUGg/j51W7yu+NQAR7rtfcCAAcY4qr5CNbooKX8E5s6da/x0Pf/882b1bf5qC8fVurpl586d4VAWLQNHYNeuXXLgwIHA6YVCwSegfsLZQurP+6yTqXb122OPPZaw0/5rrrlGWrRoIeoCKZ6t8/6kkLNUjBtyZsMZNwhoHyZBwDUCjBtcu2PIG0kAe0MkDf9/z9MKUKuWbiU5+uijzUqADRs2yM8//2w+9rzm6jBf/YGS/iSwd+9e+fbbbw2nLVu2GOPwMcccI7Vr184VUfv27WX48OHStm1b6d+/v1xwwQXG/UCuF4X85Pz5802wreOOO040QhsJAq4QyMjIkMmTJ5vngk4skSDgEoGvv/5adu/eLd26dZPDDsvzPKtLKjsj6xNPPCErV64UdT/Uq1evhOVWn+06yX3iiSeKGlCvuuoqs6Iu4Yp8esHChQtFxw4apFNXyJIg4AoBndzQtG3bNldERk4IZBLQXQnqekLHDfp3hgQBVwgsXrxY5s2bJ2qrqVq1qitih1rOfBlAldyVV15pfICqMVQfXj/++KMBqqs+1V/UaaedFmrAkcrPmjXLvCzMnj078rD5fuGFF8qjjz4qufniqlChgvG5pasuNICU+l8l5UzAzsbYPOeSnIGAvwjYPmtzf0mHNBDInYD2W13NoQFzMIDmziqVZ3///Xd56qmnTJN6Xy677LKozdsxyuOPPy5vvPFG1DKlS5c2hpZ77rknxzJRL/T5QfvMtbnPxUU8CGQSsKvu9blLgoBrBNSAr4uEdOyAAdS1uxduee3kE+MGd/pBvg2gqqoa5nQlgCbdsskLj0GR5R81DOuKAn24R0v//ve/5aOPPpKHH37YrPCMVkaPqf9VEgQgAAEIQAACEEiEwPTp0zPdwsycOVP0k1vSVeix0hdffBGrCOchAAEIQAACEIAABCDgCwKeGEAjNcH4GUnjz+9q9NRtYtb4WbRoURNASrez16hRQxYtWmRWzn7++edy9913m+3xr7/+elTXAXotKT4Cti8ykxgfL0r5h4Dtuzb3j2RIAoHYBGy/tXnsKyiRCgLqOqdkyZIxt8jqys85c+aYyVj195lbat26dW6nnTtn+6zNnVMAgUNLwPbZQoUKhZYBirtLwPZfm7urCZKHjYDtszYPm/4u6uu5AdRFCMmW2fr81HbUN8T48eOlefPmmc2qvxNNunRat7erjy6NUPnxxx/L4YcfnlmOL4kRaNSokWgQLlbNJsaN0gVPoGLFiibQiOYkCLhGQCf32Mbmv7umg/OzzjorpmC67V0NoCeddFLoomU3bNhQSpUqJdWrV4/JiQIQ8BOBMmXKGHG0/5Ig4BoB9UutvsNZ6OPanUPeBg0amMllXdRGcoMA0QlScJ80grtNL7/8chbjpz2uua7MGDhwoFkRWqJECencubOkp6dHFuF7AgTS0tKkXr16+JJJgBlF/UFAV3DUrVs34QjN/pAeKcJOQIPO4Qg+7L3ATf11HKa+2Nk54ub9C7PUduVnkSKsbQlzP3BVd53wZ+LJ1bsXbrnVZqPjBp697vSDhA2g6jNKHedPmTIlppYazW3s2LEyaNAgE7X8xhtvlFdffVV++OEHOXjwYMzrg1Jg1apVRhVdjRjP6gtdsajcTj/9dDn++ONlyZIlQUGBHhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSCmBhKcJ27VrJ2vXrjWrE88//3z54IMPogr85ZdfGr+Xy5Yti3peo8Orn8sjjzwy6vkgHdSt2Jp0RZedoY2ln25V063weu2JJ55oDKLNmjWLdRnnIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQgCCRtA9dry5cubKubNmxdR1f++PvDAAyaaeeQqT3Wkf9RRR4luS16wYIFZQarH3nrrLTnllFP+d3EAv2n0dzVoarCj/fv3J7S1qnfv3iZQ0plnnikffvhhAOmgEgQgAAEIQAACEIAABCAAAQhAAAIQgAAEkkcg4S3wkaJEGjjt8W+++SaL8VMjhE6dOtVENn/33Xdl+PDhMm3aNNGVoRqR9NJLL5X169fbywOZ6yrOv/71r7Jjxw754osvEtZRgyS99957cuGFF8p3332X8PVcAAEIQAACEIAABCAAAQhAAAIQgAAEIACBsBLIlwE0O7QDBw7ILbfckunfUyPBTpo0STp27Ji9qFSoUEGGDh0q99xzj/Tt2/eQ80E78Nhjj5nt7L169ZI1a9YkrF779u1lzJgxcfleTbjygF6gBmf1n6qrbkkQcImATi799ttvsnXrVpfERlYIGALr1q2T1atXQwMCzhHYuXOnLF68WPbt2+ec7AgcbgJ2UQp9N9z9wFXtdTGUjZnhqg7IHU4CjBvcu++eGkBfe+01mTVrlqFQvHhxef/996VcuXK5Urnjjjtk5cqV8v333+dazvWTGtlOV2+2bNlS1I/qqFGjRA3GiaQmTZoYA6hGGiPFJrBw4UKZO3eupKenxy5MCQj4iMCGDRtkzpw58ssvv/hIKkSBQHwENNChBkxk8ik+XpTyDwF1VaTunTDg++eeIEl8BDIyMkxBDUBLgoBrBNR+oLaAvXv3uiY68oacgC620nGD2rNIbhDw1AA6YsSITK11JWidOnUyf+f2pWvXrjJ9+vTcigTinPpO1eju6stT3QBoEKlEkzKdMmWKNG3aNNFLQ1feGphtHjoAKOwsAdtnbe6sIggeSgK239o8lBBQ2kkCts/a3EklEDqUBOwKUJuHEgJKO0vAPnNt7qwiCB46ArbP2jx0ABxUOE9BkKLpqct/dcWHTddff739GjPXAEE5BVSKebGDBXQFqH7ymqpWrWp8qub1eq6DAAQgAAEIQAACEIAABCAAAQhAAAIQgEBYCORpBejJJ59sIsH//vvvMnjwYNHtFrNnz870mdS8eXNp2LBhXAx3794tr7zySqgMoHGBiVGoSBHPbNcxWnL3dFpamhHe5u5qguRhI1CyZEn6bthueoD01WdusWLFpHDhwgHSClXCQMCOF2weBp3RMRgE1PWYJp67wbifYdNCn7lFixYV3m/Ddufd19eOF2zuvkbB1yBPVrQbbrjBRDVXn59DhgwRDfCj0d5tOvHEE+3XmPnTTz9tgn1Urlw5ZlkKiDz77LPy3HPPSffu3WXYsGEgyYVA48aNjRuGEiVK5FKKUxDwH4GyZcua/+NqRCJBwDUCnTp1MsEQdXcHCQIuEdDJ+1q1agnjBpfuGrIqATtxWrp0aYBAwDkCGjBZ3TdgwHfu1oVe4AYNGkiNGjUYNzjUE/L8dlKmTBnRAEbq+FWNoBr11SYdPMaTNm7caIynWrZ27drxXBLqMn/88Yfcfffdsnz5crNqdurUqaHmEUv5QoUK8TCKBYnzviWgqzm0D5Mg4BoBXcGhKzlIEHCNAOMG1+4Y8mYnwLghOxF+u0CAcYMLdwkZcyLApGlOZPx5PM8GUKuOPrD++te/yo8//igTJkyQbt26Sb169ezpXPMZM2aY7fNaKHIFaa4XhfikrgrTaPKa9OWSaPAh7gyoDgEIQAACEIAABCAAAQhAAAIQgAAEIBAXgTxtgc+p5lNOOUX0E29q3769NGnSxKz+1KjxpNwJ6HbCr776St577z3DWYMhkSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGcCXhqAM25mehnKlSoIL/++mv0kxyNSqBmzZrSr1+/qOc4CAEIQAACEIAABCAAAQhAAAIQgAAEIAABCGQlkO8t8Fmr4xcEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAf8Q8GwFqPoA/f7772XRokXmo75Bq1SpIo0aNZJTTz3VbHX3j9pIkggBDbb0zjvvmOh8iVwXrezvv/9uDq9fv15+/vln0WiVOfkyTU9PzxJcS/2fVqxY8ZBqNWrgwoULZdeuXeacRhDUSK6bNm0yfbFNmzZSqlQpc2779u3y22+/yYEDB8zvZLavQWyyJ9qHfzz9b/fu3TJt2jTRKPAacE5Tov2f/sf/v4J6/uzbt0/007Zt2ywi8Pxz5/mnN06DXOrfaU1hef6sXbtWFixYYCK6bt261egeJv2twvkdf/nx74/+XdWkY8Nkjz8LQn8dB2vasWOHybP/4/rzN7s++jun8X9B8Fd57PsH7Sc+/po3b56JC9KuXTtZs2ZNvt7/4J84f+2/NgXx+Z/M59/mzZtFA1VXrlxZmjVrJqnqf/Z+kSdOIN8GUPVJOWjQIJk4cWKurdetW1duu+02ufbaazONUblewEnfEHjyySdlzJgxnsqzevVqWbZsmamzVq1aUSMGq3uEyBcQHdRFM4Bqmfnz52eRTwNGbdiwQfShtHHjxsw+pwZYNUBFpmS1r/VmT7QP/3j6n/4htX1f+6+mRPs//Y//fwX1/FF/1TrJ1LJlyyzPdp5/7jz/tO+oMdD+nQ7L80df/HTcoPpag5myCIv+qqum/I6//Pj3x/4t1Rdh26+DNP7ThQWa7GIA8yPiH56/7jx/g/j/L1b/W7FihezZs8d8wqh/xH/VQD5/Y93/IOivOqqdIlV//yKZ8T0xAoX+f/bsYGKX/FlaL7vmmmtk+PDhCV2ufj+fe+45Ezk+oQsDWFgH1J999pksXrxYVq5cKatWrZKMjAxRRvofqE6dOtKlSxfRFYw6q1hQaenSpTJ+/PjMVZP5kePRRx81up588sny6quvmhWglSpVilqlsrADVi2gMyt2JWf2C/SlxQ76lJWuVtEZfn0YHXPMMZkPo71794oaXyNXgCar/Wj3jPbhH0//U8PDt99+a/5/1KtXz3T3RPs//e/QZyb//1Lz/2/OnDlmBejpp5+exQAK/9Tw179/+fn/361bN/M3/4033pBOnTqF6vnz008/yfLly6Vx48ZZVnGE4fmr47MBAwbIPffcI3fffXe+xl/56X/a4fI7/ovWvr6v9O7dW8455xwZPHhwUsef0dpP9vNv0qRJ5r1Mx7yzZs0y/28j/0l2+9qW6p3f54/WE+3+69hJdxXqDi+72lXLRhv/FwR/lSU/+u/cuVPS0tKkZMmSZgVkft5/XNR/7NixxvjZvXt3M/kUNv21/9gUrf8n8v7r4v23umvumv66W0b7a40aNaRVq1b5Gn/F0v/yyy+XN998U0aOHCmXXXZZJDa+J0AgzwbQ/v37i64M1KQ3XA1aGpVct73rlk01Rm3ZskWGDh1qDHr6xypyNv3KK6+UV155JcsAMwG5nS6qg5QRI0bIRx99JNu2bYupi65m7Nmzpyhz/ePvcmrdurWouwQdgKr+yUw6AMxuAE1me9QNAa8IWAOovnT/5S9/8apa6oFASgh8+umnUQ2gKWmcRvJNwBpAP//8c+natWu+63OpAmsA1dXLOgkdphRpANXvQUv/+te/RN899AVSvwct6Zj6vPPOM5P+0QygruurLtaiGUBd18vKH2kA1QUyYUuRBtBoW4jDxgN93SGgk/66u+/oo48Wu2glWdJjAPWGbJ62wI8aNcoYP4877jjRQZKuENAtb9GSlhk4cKCZoXv77bflpZdekl9++UV0ZYEaRPVYoUKFol0auGPr1q2TG2+8UT788MOEdNOZEJ25VqPp9ddfL88//7yoj1VS7gSqVatm/MnoiloSBFwiUL58ebMKXFdSkCDgGoHatWubv+9FixZ1TXTkDTkBHTeo+5Fo7nZCjgb1fU7ArhArUaKEzyVFPAgcSkDHDeqeQn3fkyDgEgFd/Keuy3LaUeqSLmGRNU9WtHHjxsnZZ58t7777rsT6Q3vCCScY46du87755puND9C+ffualaF6vVrKH3nkkcDzVn+UnTt3Ns71VVn1D6EzBbqVo2bNmmbbg259UJ46C6gDcP2o8VMd8s+YMcNskX/55ZdNYAL1yckfidy7jT6Q9EOCgGsEdPa7Y8eOromNvBAwBJo3bw4JCDhJQFfd64cEAdcI2AmnWO9lrumFvOEg0LRp03AoipaBI6CGT4yfbt3WPBlAp0+fLu+//35M46dFoVHg1e+n+ixQo90LL7wgahhVXzy6gvT8888X3Rod5KS6qhH4lltuMUbgFi1aJKyu+r95/PHH5cUXXzQG5D59+iRcBxdAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCBMBKLvW8+FgAaP0QiZiczU6KykGk0jk/q0VB81uj354YcfjjwVuO8azU59aU2ePNkYf/Ni/FQoujVLt7+rn5RBgwZlBvIJHDAUggAEIAABCEAAAhCAAAQgAAEIQAACEICARwQSNoCqr0/1jaS+DuJNX375pfHrkb18/fr15bHHHpNPPvlE5s6dm/10YH5/9913xnenjaaaX8U0KIHeA43OToIABCAAAQhAAAIQgAAEIAABCEAAAhCAAARyJpCwAVSr0u3q/fr1k4MHD+Zc8/+f0dWiWk6jY2lEzWjpmmuukXbt2sl7770X7XQgjmnwo2bNmnmqS4cOHWTlypWe1kllEIAABCAAAQhAAAIQgAAEIAABCEAAAhAIGoE8GUBvuukmGTlypHTp0sX4Al27doyUYgEAAEAASURBVG3mduy9e/eaID3q51MjwD/zzDOGWU4BPTQC/JAhQ2T8+PFBY5upT40aNeSHH37I/J3fL2p4/umnn3I0Kue3/qBcr5w0oBQJAi4S2LVrV+Zz1UX5kTm8BPbt2yd79uwJLwA0d5YA4wZnbx2C/5dArMUpgIKAHwkwbvDjXUGmeAlgb4iXlD/K5ckA2r59e2PY/Prrr+XCCy80kbY1avERRxwhmjdo0EBuu+020a3fmnT151133ZWjxn/5y1+MX9H9+/fnWMblE7pa8/XXX5ePP/7YEzUefPBBUb+q5cuX96S+oFYyf/58Y1hfv359UFVEr4ASyMjIMH6DZ8+eHVANUSvIBHRs8MUXX2DAD/JNDqhuCxcuNOMGndgnQcAlAjt27DDibtu2zSWxkRUChsC0adNk4sSJElRbALc5uAQ0yLUu5EtPTw+ukgHTLE8GUGWg0cxfeukl44tSf+vMjQZHyj7zqMbPUaNGScmSJbVYjunkk082UdJzLODwCfV1etZZZ8m5555rDMb6Yqi8Ekn6B2HChAnSrVs3UQPoddddl8jloSxrZ2NsHkoIKO0kAdtnbe6kEggdWgLab3UFKC8yoe0Czipun7k2d1YRBA8dAbvqnudu6G59IBRWA77uIk30/TgQyqOE0wTs5BPjBnduY5H8iHrjjTfKJZdcIoMHD5Zvv/1WFixYIL///rtZEXrUUUfJpZdeKldddZVo4KRYSSPBly1bNlYxZ88PGzbMBHp6//33jduAcuXKGb+gulq2Tp06UqpUKUlLSzOGYn34b9++XfQ/1KpVq0SjyKsf1U2bNhn9BwwYIDfccIOzLBAcAhCAAAQgAAEIQAACEIAABCAAAQhAAAKpIpAvA6gKqduwH3rooUx5deaxcOHCmb/j/aJRzYOcVL/JkyebaPBjxoyRLVu2yPTp080nXr11FW2fPn1k0KBB8V4S6nLW8J6X/hhqcChf4ARs37V5gQuEABBIgIDttzZP4FKKQqBACdg+a/MCFYbGIZAAAdtnNbYCCQKuEbD91+auyY+84SVg+6zNw0vCHc3zbQDNrirGpuxE/ve7atWqMnr0aBk3bpwJIqWG0K1bt/6vQA7fKlWqJDfffLNo8Cn9ToqPQKNGjcyq4ipVqsR3AaUg4BMCOmHSokWLTBcjPhELMSAQF4G2bduabWyMB+LCRSEfEWjYsKHZkVO9enUfSYUoEIhNoEyZMqaQ7igjQcA1Am3atJHdu3ebGBeuyY684Sagu3l1kZoGvSa5QcBzA6gbaheslN27dxf9aJRndfi8aNEi4zh3zZo1osFP1FBaq1Yt86lZs6a0a9cupg/VgtXIn62rS4F69er5UzikgkAuBHQFR926dXMpwSkI+JcAE3X+vTdIljsBfYlRv+0kCLhGwK78LFKEVzvX7h3yChP+dAJnCZQoUYJxg2N3j7+SBXjD9D/MmWeeGVUCdWa+bt06KV68OMbPqIQ4CAEIQAACEIAABCAAAQhAAAIQgAAEIACB2ARiRyeKXQclkkDgxx9/FF392aNHjyTUTpUQgAAEIAABCEAAAhCAAAQgAAEIQAACEAgHAQyg4bjPaAkBCEAAAhCAAAQgAAEIQAACEIAABCAAgVASwAAaytuO0hCAAAQgAAEIQAACEIAABCAAAQhAAAIQCAcBDKDhuM+h1HLHjh2yZMkS2b9/fyj1R2l3CRw8eFB+++032bp1q7tKIHloCaj/6tWrV4dWfxR3l8DOnTtl8eLFsm/fPneVQPJQEtBxgyb6bihvv/NKr1+/XlatWuW8HigQPgKMG9y75xhA3btnSBwngYULF8rcuXMlPT09zisoBgF/ENiwYYPMmTNHfvnlF38IhBQQSIDADz/8IDNnzmTyKQFmFPUHgUWLFsm8efMw4PvjdiBFAgQyMjJM6e3btydwFUUh4A8Cs2bNku+//1727t3rD4GQAgJxEtDFVjpuWLlyZZxXUKygCfgiCryu0JswYYJ07969oHnQfoAIHDhwwGhj8wCphioBJ2D7rM0Dri7qBYyA7beaFy5cOGDaoU6QCUT23SDruXbtWrPSNVLH5cuXm5/6Ejdt2rTIU1K3bl2pVq1almP88BcBuwLU5v6SDmkgkDuBsDx7c6fAWRcJ0Hfdu2u+MICq8fPbb7/FAOpe/0FiCEAAAhCAAAQgAAGHCLRq1UrWrFkTVeKRI0eKfiJTuXLlZOPGjUxoRELhOwQgAAEIQAACzhFIigFUl6/rFjjdfqxLgjdv3hwVjJZTX2Fff/213HPPPVHLhPVg2bJlpUuXLtK0adOwIsi33mlpaaYOm+e7QiqAQIoIlCxZ0rRE300RcJrxlID22127dmEs8ZQqlaWCgH3m2jwVbRZEG5dddpl88803WZpWv+lLly6V2rVrS+nSpbOcU4Mpq7mzIPHdj+LFixuZuE++uzUIFAcBfebq6uUiRZJimohDAopAIG8E7HjB5nmrhatSScDzp4zOEJ9//vny1VdfpVKPwLV11FFHyeTJkwOnVyoVaty4sdSpU0dKlCiRymZpCwL5JqATIOoSpFixYvmuiwogkGoCnTp1Mi8yhx2Gm/FUs6e9/BFo2LCh1KpVK/Djhqeeeip/oLjadwTsxGl247XvBEUgCEQh0LFjRzNuwIAfBQ6HfE2gQYMGUqNGjcCPG3x9ExIUznMD6CWXXILxM8GbQPHkEChUqBAPo+SgpdYUELCrOVLQFE1AwFMCrODwFCeVpZAA44YUwqappBDQPkyCgGsEGDe4dseQN5IAi60iafj/u6cG0N27d8uUKVOM1rq95swzz5R27dpJ5cqVo5LYt2+fqCP2Rx99NOp5DkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyA8BTw2gy5YtM36/OnfufIgD9ZyELF++vDzxxBPyyiuv5FSE4xCAAAQgAAEIQAACEIDA/7F3JvA2VV8cX8g8j+GZZ5LZ3yyUIUkUDSIkISFJoqTy16QSqWgQEdU/icyRIUNmhTKTzJ7IPL//+63/f9/uu+++O7x77n333Ptbn899595z9tl77e8997x91l57LRIgARIgARIgARIgARJIFgFLA3QhcDpift16661+KZM/f37p1auXX+ewMAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAl4I2CpARTxD9q3by+///67t3YTHWeyhERIuIMESIAESIAESIAESIAESIAESIAESIAESIAESCBAApYaQKHLiBEjZOvWrbJq1SqfVUMc0Pfff9/n8ixIAr4QOHLkiCxfvlzOnz/vS3GWIYGwIYB4yitWrJADBw6EjU5UhAR8JbBlyxZZv369r8VZjgTChgDGoxg3nDt3Lmx0oiIk4AuBq1evarGLFy/6UpxlSCCsCGzbtk3Wrl2rmeDDSjEqQwJeCBw/flzHDWfOnPFSkofDhYDlBtCSJUvKggULdEn7nj17NCbopUuX3G7Pnj0rKPPcc8/xhhcuV0QE6QED6KlTp+TkyZMR1Ct2JRoInD59Wq/bQ4cORUN32ccIIwDDPa5d80AeYd1jdyKYgBk3xMbGRnAv2bVIJGAm+zGBSiEBuxHAuAH33ytXrthNdeob5QSOHj2q9gaOG+xzIfidBAnL25HZ3ZvgH3CpUqW8FXMcHzZsmOM935AACZAACZAACZAACZAACZAACZAACZAACZAACZCAFQT8NoCWL19e8uXLJ/v27bOifdZBAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAArYhkCpVKtvoSkX/R8BvAyhOa9WqlcbsrF27thQrVkyS+8VjeRziJqxcuZLfBwlYTqBgwYIa/zN37tyW180KSSCYBHLkyCF58uSRmJiYYDbDukkgKASKFi0qWAWSNm3aoNTPSkkgWAQwbkB4Jtx/KSRgJwKZM2dWdZGQlkICdiOAcQPCOKRLl85uqlPfKCeQP39+QeiyvHnzRjkJ+3Q/WQbQu+66S3bs2KGxPq3o6qxZs2Tjxo1WVMU6SMBBADckvCgkYDcC6dOnl3r16tlNbepLAkqgYsWKJEECtiSAFU54UUjAbgTMhBMNoHb75qgvCFSoUIEgSMCWBGD4pPHTXl9dsgygjRo10gQHVnX1zjvvZKZuq2CyHhIgARIgARIgARIgARIgARIgARIgARIgARIgAQeBZGWBh3fSo48+6qjE+c3+/ft16ZvzPk/v+/btKxcvXpSHHnrIUzEeIwESIAESIAESIAESIAESIAESIAESIAESIAESIAG/CSTLAOqplTp16simTZs8FUlwrFOnTtK2bVu5cuVKgv38QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKBErDcAOqvQtWrV9ckSOPGjfP3VJYnARIgARIgARIgARIgARIgARIgARIgARIgARIgAY8EUtQAeurUKXn11Vd1yfzixYs9KsqDJOAvgbi4OA2v4O95LE8C4UDg0qVLcuPGjXBQhTqQgF8Erl27xlUdfhFj4XAhwHFDuHwT1CO5BHANU0jAbgQ4brDbN0Z9nQkgnCPFPgSSlQTJuXsPPPCATJ8+3bHr+vXr0qRJE7nppv9VnSpVKnH3wsP9+fPnHedlz57d8Z5vSMAKAtu3b5edO3dK3bp1mZ3NCqCsI2QEzpw5I0uWLJGiRYtKlSpVQtYuGyIBKwgsX75cJzabN28uqVOn6DyrFd1hHVFEAGMGjB1q164tN998cxT1nF21O4ELFy5oF86dO2f3rlD/KCSwcuVKtQtg3JAmTZooJMAu25XA7t27Zdu2bfKvf/1LChQoYNduRJXeARtAv/rqKxk9erQMHDhQrl69qvD8tYJnzpw5yaRKUfVtsLOWEjDXodlaWjkrI4EgEjDXrNkGsSlWTQKWE8B1C28OTIjSAGo5XlYYRALmnmu2QWyKVZOApQRMLgXcdykkYDcCMODDjoCxAw2gdvv2oltfM/nEcYN9roOADaDoar9+/aRGjRrSqlUrOX36tFStWlWyZcvmkQIeigoXLiylS5eW++67T8qXL++xPA+SAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgL8ELDGAotF69erJ7NmzpVmzZvLBBx/o8iF/lWF5ErCSgPE84kyilVRZVygImGvXbEPRJtsgAasImOvWbK2ql/WQQLAJmGvWbIPdHusnAasImGsWYccoJGA3Aub6NVu76U99o5eAuWbNNnpJ2KfnlhlA0WUYQWfOnJms+AdHjhxJ1nn2QU1NQ02gTJky6omcP3/+UDfN9kggIAJ58uSRSpUqCbYUErAbAawI4TI2u31r1BcEsCoJYZliYmIIhARsRSBr1qyqL65fCgnYjUD16tU1dnjatGntpjr1jXICpUqVkowZM0qhQoWinIR9um95doI77rhDE3f4g+Dvv/+Wjz76yJ9TWJYEvBLIlCmTlChRgrFkvJJigXAjAA+O4sWLi3mgCTf9qA8JeCKQN29eTmh6AsRjYUsADzElS5bkuCFsvyEqlhQB4/lpktAmVY77SSAcCWDCnxNP4fjNUCdvBDJkyKDjBt57vZEKn+OWG0D97VpcXJwaP7GlkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkICVBJK1BP7FF1+UpUuXytChQ6Vp06YOfZAACcvgfc1AiGxvsbGxcubMGRk2bJijHr4hARIgARIgARIgARIgARIgARIgARIgARIgARIgASsI+G0A/fXXX2X48OHa9oABAwSfjeTIkUMKFiwoixYtMru4JQESIAESIAESIAESIAESIAESIAESIAESIAESIIEUI+C3ARRx6bJnzy6I21mlSpVEij/yyCNqAEUcJRhDERchKYEH6IkTJ+TUqVNJFeF+EiABEiABEiABEiABEiABEiABEiABEiABEiABEkg2Ab8NoEjKsXfvXtm8ebPcdtttiRq+5557JF++fLJx40afgxmPHDlSzp8/n6gu7iCBQAhcuHBBjhw5IsWKFWNCg0BA8tyQE0BM5H379gmSyTARUsjxs8EACRw/flyzwGMSlEICdiJw8eJFOXTokI4bmNDATt8cdTW5FK5du0YYJGA7AnCIunLlis+2A9t1kApHLAGOG+z31SYrCVKuXLmkSZMmbo1K2bJlky5duvh1A+vatav9yFHjsCewc+dO2bp1qxpBw15ZKkgCTgQQG3nLli3y22+/Oe3lWxKwB4ENGzbIunXrfI4Hbo9eUctoILBr1y7Ztm2bHD58OBq6yz5GEAHkU4DQoSSCvtQo6gocp9avXy9YHUohATsR2LNnj44bDh48aCe1o1rXZBlAvRHr06ePtyIJjufJk0fatWuXYB8/kECgBG7cuKFVmG2g9fF8EggVAXPNmm2o2mU7JGAFAXPdmq0VdbIOEggFAXPNmm0o2mQbJGAFAeMBarZW1Mk6SCBUBMw912xD1S7bIYFACZhr1mwDrY/nB59AUAyghQoV8lvzihUr+n0OTyABEiABEiABEiABEiABEiABEiABEiABEiABEiABTwQsN4DS/dcTbh4LJYFMmTJpc2YbyrbZFgkEQgBJ5CC8dgOhyHNTigCu23Tp0rkNk5NSOrFdEvCFgLnnmq0v57AMCYQDgfTp06saadKkCQd1qAMJ+EUA99y0adMKYy/7hY2Fw4CAGS+YbRioRBW8EPA7CZKn+q5fvy6NGzeWL7/8UqpXr+6pKI+RQNAJlC1bVhMZZMiQIehtsQESsJIAYim3aNFCjUhW1su6SCAUBBo0aCBYhpk6teVzrKFQn21EMYHSpUtLkSJFhOOGKL4IbNp1M3GaJUsWm/aAakczgXr16um4gQb8aL4K7Nn3UqVKCVY/c9xgn+/P8qeT3bt3S40aNQQPQFOnTpXLly/bhwY1jSgCqVKl4s0oor7R6OoMvDlwDVNIwG4E4MEBTw4KCdiNAMcNdvvGqK8rAY4bXInwsx0IcNxgh2+JOiZFgMbPpMiE537LDaAxMTEyZswYqVmzpgwePFgt4gMHDhQYRikkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkFwCL730kq40gdGfLzKIhmtgypQp+nM5cOBAcn82PC+egKUGUFx4L7zwgiAL/DvvvCP79++XyZMny86dO6V8+fLStGlTmT59uly7do3wSYAESIAESIAESIAESIAESIAESIAESMAvAqtWrdJl836dxMIkEAEE/vzzzwjoRcp1wVIDKOJ99ezZ09EbGEQRx27mzJmyd+9eqV27tjz55JNSuHBhNZT+8ccfjrJ8QwIkQAIkQAIkQAIkQAIkQAIkQAIkQAK+EFi4cKEaQhF7nC8yiORroGPHjr78JFjGCwFLDaCe2oLRc/jw4QKX3dGjR8vEiROlRIkS0qpVK5k9e7bcuHHD0+k8RgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJ+EwiZARSaHTp0SEaOHCnPP/+8vofRc86cOXLffffJ66+/7rfyPIEEPBE4cuSILF++XM6fP++pGI+RQNgRQPK4FStW6IRR2ClHhUjAC4EtW7bI+vXrvZTiYRIIPwLHjh3TccO5c+fCTzlqRAIeCFy9elWPXrx40UMpHiKB8CSwbds2Wbt2LZe0h+fXQ61IIKIIWGoAvX79urz88ssJAF25ckXjfrZs2VKKFi2qxk+TEKlkyZJq+Dx48KAMGTIkwXn8QAKBEoAB9NSpU3Ly5MlAq+L5JBBSAqdPn9brFpNGFBKwGwGs9MC1ax7I7aY/9Y1eAmbcEBsbG70Q2HNbEjCT/ZhApZCA3Qhg3ID7L+wGFBIgARIIJoGbrK582rRp8vDDD+vD+5dffinIVuU8kLzpppukdevW0qNHD02KhDihFBIgARIgARIgARIgARIgARIgARIgARIgARIgARIIBgHLDaA7duyQ0qVLJ9K1SJEi0r17d+nWrZsUKFAg0XHuIAESIAESIAESIAESIAESIAESIAESIAESIAESIAGrCVhuAHVVEMZOuLQ3bdpUHnzwQRo/XQHxc9AIFCxYUON/5s6dO2htsGISCAaBHDlySJ48eSQmJiYY1bNOEggqAYS7wTLMtGnTBrUdVk4CVhPAuOHs2bN6/7W6btZHAsEkkDlzZq0+Q4YMwWyGdZNAUAhg3IAwDunSpQtK/ayUBEiABAwBS2OAmkphcHrxxRc1gcfhw4dlyZIlgjif5cqVUyPo5s2bTVFuSSBoBPLnzy8NGjQQMygMWkOsmAQsJpA+fXqpV6+ewHOeQgJ2I1CxYkWpXr263dSmviQg+fLl03FDlixZSIMEbEXATDjRAGqrr43K/p9AhQoVpGbNmsLQeLwkSIAEgk3AcgNoqVKlBBlgkQypcOHCqn+jRo1k/vz5mhU2derUeoNDUqSffvop2P1j/SRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAlFMwFIDKGZthg0bluQy9ypVqsjUqVMFcUJPnDghDRs2lPr168ucOXOi+Ctg10mABEiABEiABEiABEiABEiABEiABEiABEiABIJFwFIDKLw7O3bsmKSuJ0+elLffflvuvPNO9QZFwZUrV8rdd9+tHqNJnsgDJEACJEACJEACJEACJEACJEACJEACJEACJEACJJAMApYmQYqLi5PVq1dL3bp1Hapg39KlS+WTTz6R6dOna2IEcxCxQrt27SqPP/6428zxphy3JEACJEACJEACJEACJEACJEACJEACJBCJBF577TWZNm2aX11btGiRxq/266QQFsZKX6wARohEZ0Hc4rx586ruiAHbtm1bKVu2rHMRfT9jxgwZNWqUnD59Wj9jxXHr1q2lS5cuUrJkyQTlX331VZk9e7acO3dOcubMKc8++6zcddddCcrwAwlYagC9ceOGPP/88zJr1iz5448/5JtvvpHJkyfL3r17E5DGsveePXtKu3btBMk+KCQQDAIwvl+6dEkyZswYjOpZJwkElQCuXWTDhGc9hQTsRODatWuC8QCzudrpW6OuIMBxA68DuxPANUwhAbsR4Ljhf98YbCYIEzhixAhNJpkrVy757rvvpG/fvlKoUCF1NMPzwdGjR9W5bNKkSWrsQwK/cBUYIPGCkfP333/X8Ifff/+9XL16VW1Ev/32mxo4Bw8eLNWqVZMPPvhAatWq5egODKM1atQQJNg8c+aMNG7cWIYPH+447vxmyJAh+n/8lVdekV9++UUTcDsf53sSAAFLDaCoEN6eOXLk0IcffDaSPXt26dSpkxo+b7nlFrObWxIIGoHt27fLzp071SMZM0wUErALAfyDX7JkiRQtWlQQO5lCAnYisHz5cl3t0bx5cxrw7fTFUVcdM2DsULt2bbn55ptJhARsQ+DChQuqKzyfKCRgNwIIiXf+/HnBuCFNmjR2U98yfS9evChjx46V++67z1EnjKCQm266SY2geI+k0/Xq1VMjHwyiKSFwdps3b57adnxpH96dMICWL19ebUU4B8/nMHZ27txZnea6deumz+0wgvbo0cNRLRJr9+vXTw2fmzdvFtzvMmXK5Dju/AaeorA5lStXznk335OAg4DlBlDUDM8PIzVr1tQfxoMPPpjkhWrKcksCVhLAPxGI2VpZN+sigWASMNes2QazLdZNAlYTwHULb47r16/TAGo1XNYXVALmnmu2QW2MlZOAhQSuXLmiteG+SwlvArt27VKPN3jAGcH/TAi+x+7du5vdusVqSXjHxcTEJNgfSR9g0AIPcIhmAyj67uuSbSwFh33l8uXLIb8UcJ3ef//90qpVK5/bhgE3KcFqNxhB//zzTxk6dKg8+eSTaihFwmwjMIC+9dZbcurUKZkyZYqGUDTHzBY2qK+++kqwbJ5CAkkRSPpKTOoMH/ZjyfFDDz0kvXv3VldmH05hERIgARIgARIgARIgARIgARIgARKIWAIw3nz44Ydu+wcDNvJmuAq85/r06eO6m58jjACWtPsjgwYNSlB8//796iUKY+O2bdukYMGC6ghkJkgQFxOrcvH58OHDjnOxhN7VoxLGRHjmYn+lSpXUQI33MLg+8MADsnbtWl2OjiX7CHmEegMVLGGfP3++totl7j/88IOjSuSOgZF03Lhx8u677+pEAYzAzvLjjz9Knjx5NHyA836+JwFnApYbQMuUKSPLli2T/PnzO7fD9yQQcgImdmI0zySGHDobtISAuXbN1pJKWQkJhIiAuW7NNkTNshkSCJiAuWbNNuAKWQEJhIiAuWZdDQIhap7N+EFgwIABUqJECTUoOZ+2Z88eQWKYIkWKOO+WDBkyJFgSneBghHww16/ZRki3QtINhA749ttv5YsvvlCDIZamP/bYY7JgwQIpVqyY7u/Vq5f8+uuv8vrrrwuMpjBiLly4UHO3xMbGyn/+8x/NzWIUfv/992XDhg3qYYpl6/D0hAEeBkoYaXfv3q1FZ86cKVu3btVl7PDcDFTw/bds2VINoIsXL5YjR45IgQIFHNX2799fxo8fr0vp586dm8hbduLEiYk8qB0n8w0J/J+ApQZQXLRwO6bxk9dXOBCAMT5btmy8HsPhy6AOfhHA7CVmW7GlkIDdCCBYfbQvY7Pbd0Z9/0egdOnSkjlz5oheasrvOjIJZM2aVTuG65cS3gTwbAJPNso/BKpXr65GORiAKf4RQNxfcEMeFnhtDhs2TI2AiB0KQ2fdunXl4YcfVgOoqRn3i8cff1xWrVqlBk2zH1vkz0DSJWxNlnXUBQMqBOfBgxQG0a5du2qmdT1g0Z9bb71Va0JCNxhanQ2geLa/++67NeH222+/ncAAivwJ8B6F8ZZCAp4IWJpeGLOOTNjhCTePhZIA3PQxw0oP0FBSZ1tWEMC9tHjx4mIeaKyok3WQQKgIIKi984A1VO2yHRIIlABCOOGBj+OGQEny/FATMJ6fnuLshVontkcCvhLAhH8kxzj1lUNyyiFhH2KBIjESpFmzZuoxPHXqVJk+fbruS2p5OpbEuwo8kWFIXbNmjePQPffc49Ypw9x3HAUteIMkSUaOHTtm3jq28KCGIFnspk2bHPvhhAdP1aT66ijIN1FPwFIDaNTTJAASIAESIAESIAESIAESIAESIAESIAESCBEBGEIhzomDktN0o0aNdAUlvJRfeeUVjSEKD1N4hYZCkAjJCDw+XQX9w0ojyDvvvOM4/Nlnn3H5u4MG33giQAOoJzo8RgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIRTgArIT766CONPYvl9AgN45yMyLn7wfAARexRCMJ53HLLLc7NOd4//fTT+h5en4cOHZIdO3bI6dOnpV69eo4yfEMCSRGgATQpMmGwH0GNDx48KH/99VcYaEMVSIAESIAESIAEIonAqVOnBAkTnF8XL17ULmLpmfP+AwcOCDIUU0iABEgglAQQ43D16tXa5NmzZ2XdunW6RDeUOrAtEogmAsjyvnHjRqldu7YaGFu0aCGjR49OhCCYBtB//etfSYajad++vRQuXFgTiY0ZM0Ymxic/QuInCgn4QoAGUF8opVCZKVOm6I8bwYYpJEACJEACJEACJGAVgb1792qsVmSJdX799NNP2sQjjzySYH/RokWlQ4cOVjXPekiABEjAI4Fdu3bJQw89pLEHTdKgo0ePCgwjiDMN7zQYRCkkQALWEUDyIzhhwfMTSZKQdR1xsZEB3vX3ZrUBFHE9Z8yYod6f7777bpKdQqzjfv366XF4q3755ZeCMQuFBHwhQAOoL5RYxpYELly4IAjkTI8VW359Ua00Mh/COOE60IhqKOy8bQgcP35cDh8+bBt9o1VRJAqoXLmywLDp/EK213Tp0kmhQoUS7IeRNNITXcL7FVlnr127Fq2XBfttUwIYN0Ai5dr9/PPPBdmgYdhAn8y9J0uWLIK4gPg/g/iEuIdt3brVpt8a1TYETpw4oZ6G5jO3/xAwv+lAnmexnByClaXOYlaZIqu7ESQWmjlzpn6EgROOWC+//LLguXrLli26P0OGDLpF5nVfBYmVPMkvv/wibdq00cz18OisVKmSp+Ia7xPJYrH0HZMiSKRFIQFfCNAA6gsllrElAcxgYVB05MgRW+pPpaOXQGxsrA4yfvvtt+iFwJ7blgDiN2GJYiCDddt23kaK586dW7O87t+/X5xfJ0+e1AcQJCJw3r9v3z4ZPHiwjXrov6rwONu2bRsN+P6j4xkpTMAYIuC5ZXfBCjh4fF6+fFm6du0qCL/x9ddfa7fg+Yl4f/BMQyIU3JeQtAWTxhT7EsBy6/Xr1+uSZvv2Ijia4/qHwEiM30RSgliYEHcTApgogGBCYf78+RpSok+fPmKyrGO/83ljx45N0Fbq1KklX758DqNkyZIltT5kmcek4RdffCEIqeNJjPHVeYIc48Tff/9dBg4cqL9jJFv6+OOPpV27dp6q0mPZsmVzLHvv3r271/IsQAKGwE3mDbfBI4Af8ttvv+13A5jRgCxcuFDKlSuX6Pzt27cn2scd/xAwM01m+88RviOB8CZgrlmzDW9tqR0JJCRgrltssWyKQgJ2IeB87dpFZ+pJAiBgPEDN1q5UMOlijBmIOWgyT2Nywlnq1KkjK1asEMQC/P777wUxC9euXStWL8l1bpPvg0eA997EbDGRjMmASZMm6cFLly5J8+bNpVmzZmowhLEQAmeJUaNG6cQAPg8YMEBDRzhPWMKbGraIN998U+688071mIRXJ5yFEOu7WrVqCeLqwjMU7Tz44INqJF26dKnMmjVL4IENqVu3rtSsWVMnu7GdNm2a5MyZU4+5/pkzZ45MnjxZf584hs/58+cXJFtKnz69lChRQsqWLSuvv/66tofVKb4KlsHPnj1bbr/9dl9PYTkSkBQxgOIHjQsdlvtoENygMFuZXMEy2EDOT267PI8ESIAESIAESIAESIAESIAEQkHgpZdeEhh6OnXq5DB+JtUujCdTp06VChUqqPcgvNF88RxLqj7uJ4FwIgDDIl7ukg8564nrH85WeHkSZE7HhALCRxQsWFCLNmjQINHvrG3btjqhAG9TeFhjmTl+l86C396aNWvU8xohdBCTMym56667BC94mVotaHvRokWc+LAabITXl/TVGmDH//77bxk3bpz8+uuvEhMTozMOpkrEWKpatar06tVLnnnmGbM7YrddunTRGRPE0EAmw5YtW3q8URgQWM6BOBxFihQR3KAo/hHIlCmTnmC2/p3N0iSQcgQwKwrhtZty3wFbTj4BXLd4gKX3Z/IZ8syUIWDuuWabMlqwVRLwnwAMEhA733dhcIERE16ciO/pi8Aj7bnnnpPevXvrMlwaQH2hFn5lcM+F97InQ1r4aW0/jcDXGD+hvYkN6twTxACH4J7ibgWqKYvfqVkKb/alxBZ2EgoJ+EMgKAbQxYsXC4x+JtbDPffck0Cnhg0byrx583Q2AAa+CRMm6I8sQaEI+4B/yLVr19YMZchgiFgZ3m4ayLrWs2dPnf2BCzzFPwLwMi4Wn7TBBGr272yWJoGUIwDv+BYtWmgikpTTgi2TQPIIYMIODzKIGUUhATsRQNZbPExx3GCnb426goCZODVLVO1IZfPmzeoogniFGL/7Kq1bt1YDKJbEU+xJoF69ejpusLMB357kqTUJRB8By59OEKgXGbyM8TMppMjgt2zZMpkxY0bEB9U3DJBRFW7a9957r8be+Oyzz8whboNAADNTfIgJAlhWGRICmHllLKuQoGYjFhOAh4GJTWVx1ayOBIJKgOOGoOJl5SEgYOdxg0laWrx4cb9IYaUh/ucggdvVq1f9OpeFw4MAxw3h8T1QCxKIBgKWG0ARXwLLvOHKjuC5999/f5Ic4YL98MMPa2wLBK6OBoFHzLPPPis//PCDvPHGGxqrBoGGKSRAAiRAAiRAAiRAAiRAAiQQjQSMF+uFCxf86j6MnteuXVMjKJdQ+4WOhUmABEgg6ghYbgCFhyMyicEDdMGCBdKhQwePUCtWrKhZxxDEOpoEjDZu3Ch58uSRSpUqqWdoNPWffSUBEiABEiABEiABEiABEiABEEA2aMgvv/ySICO17vTwB0vnEXYFy+bt7AHroYs8RAIkQAIkYBEBSw2ghw8fFiQ/Gjt2rOTMmdMnFU+dOqXlkCwp2gReskgU9f7776sn7IABAwQBwCkkQAIkQAIkQAIkQAIkQAIkEC0EEIO3VKlScuzYMZk7d67P3UYuCQiSzFJIgARIgARIwBMBSw2g+fLl0yDc8Gj0VVatWqVFT58+7espEVcOSaIwe7l161aNDbpt27aI6yM7RAIkQAIkQAIkQAIkQAIkQAJJEejbt68eQkg1ONV4EzxHfvrpp5o9vFevXt6K8zgJkAAJkECUE7DUAIq4K1WrVlVDni9cv/vuO42FibI1atTw5ZSILVOgQAGZP3++dO3aVerXry9jxozR5RwR2+EQdAzB1JcvXy7nz58PQWtsggSsIwBPcGQzRVI5CgnYjcCWLVtk/fr1dlOb+pKAep5h3IBY9hQSsBMBk/zn4sWLdlI7ka49e/aUKlWqyK5du9Sj8/jx44nKmB0YJ8GJBPE/YTBFgl2KPQnA+Qf5QBDKgEICJEACwSRgqQEUirZr105eeukluXTpkke9Z86cKV26dHGUqV27tuN9tL5B3JqnnnpKli1bJh999JG8+OKL0YrCkn7DAIoQC8gKSSEBOxGARzyu20OHDtlJbepKAkoAhntcu+aBnFhIwC4EzLghNjbWLipTTxJQAmay3+6htJDNHc+IRYoUEXh3litXToYPHy47d+7Ufl6/fl2WLl2qDiONGjUS/FZhBH311Vd5JdiYAMYNuP9euXLFxr2g6iRAAnYgcJPVSsKA99NPP0nDhg0Fs3gmGPXZs2dlx44d8vvvv2vcS7P0He3fcccd8sgjj1itim3rQwgBeM+MGDFC9u3bp8vibdsZKk4CJEACJEACJEACJEACJEACPhCA8XPdunXSuXNnXR3n7BCyd+9eady4sdaSLl06GTRokBpIU6e23KfHB01ZhARIgARIwG4ELDeAwuA5adIkad++vXTr1k15YGl8tmzZ3LJBNvRvvvlGY7e4LRClOzNkyKD/0KO0++w2CZAACZAACZAACZAACZBAFBJAXol58+bJkiVLZMqUKRoy7c8//xQYOitXriwtWrSQ7t27S/HixaOQDrtMAiRAAiSQXAJBmS7LmjWrztjhHxf+SSE2i6uULVtW/6Fhhi979uyuh/k5CQKjRo2SYsWKqXdtEkW4+/8EChYsKLly5ZLcuXOTCQnYikCOHDkkT548EhMTYyu9qSwJgEDRokWlUKFCgqWMFBKwEwEzbsD9l0ICdiKQOXNmVRcOFJEk8PZEkqPFixdrt0qWLCkbN27UJe80fkbON41xA+6/8OqlkAAJkEAwCVjuAeqsLGbn8MLy9+3bt8v+/fsFyX4QpBozexT/CCAu4HPPPafxUcaPHy8dO3bUhEn+1RI9pfPnzy94UUjAbgTSp08v9erVs5va1JcElEDFihVJggRsSQBjU45PbfnVRb3SZsIp0gygUf/FRgmAChUqRElP2U0SIIGUJmC5ByiMnK4BuOERWrNmTV0WjwznzoPLvn37ypkzZ1Kagy3aRxgB4xGGgQ5mQSkkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJJE7DcAFqnTh3ZtGlT0i26HOnUqZO0bduWWd9cuLj7iLg3yBA/cuRIWbt2rXrTuivHfSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAv8jYLkB1F+w1atXl5UrV2pmeH/PjcbyhQsXlmeeeUaqVKkSjd1nn0mABEiABEiABEiABEiABEiABEiABEiABEjALwIpagA9deqUBrHGknkT3Nov7VmYBEiABEiABEiABEiABEiABEiABEiABEiABEiABDwQCDgJ0gMPPCDTp093NHH9+nVp0qSJ3HTT/6pOlSqVuHtdunRJzp8/7ziPmeAdKMLuzZEjR2TJkiUSFxcXsG5//fWX1nHu3Dn5888/BVkrkandnaAMjOQQXEPI5p4xY0Z3ReXEiROCawqSJk0aTX6Ec7DP+Zxr167JsWPH5MaNG1o2mO0jZIGrsH3y9/X6O3nypN4jzf3T3+uf1x9/fyl1/zEZtF2zufL+x/ufr/e/QP//J/f+h3EOxg0YywYy/khu++Y3m1L9j+T28Z1C8OwR7PFnSnz/x48f1/6Z8a1+cPpj9/uvU1cSvHU3/k8J/lDKPH+wff/HX7g+ce1i3ODP/S/BxcAPJEACJOADgYANoF999ZWMHj1aBg4cKFevXtUmL1686EPT/xSBEerRRx/9ZwffhRWBXr16ycyZMy3Vae/evbJx40ats2XLlmKyVzo3gjinZ8+edey6+eabpXbt2o7P5g3KrFq1ynzUbdWqVXWQu3PnTqlbt67kzZtX9+/Zs0e2b9+eoGyw2i9SpEiCdvCB7ZO/L9cfEsOtWLEiwfXj7/XP64+/vwQXUAjvP8hCjAeZ5s2bi/ODIO9/vP/5cv/DdRvo///k3v8wZoCOmDh1Hsvy/uvf+Cu5/M09KxjfvzEQYiI+2OPPlOj/rl27FF9SiWXtfv/FJLCrJDX+Twn+Rjc8f7B9/8dfCIeHyQmMG3z9/cNoSkmaAJ61S5QokXSBEBzBBAVeFSpUCEFrbIIEfCMQsAEUzfTr109q1KghrVq1ktOnTwtu/shY7knwUIR4lqVLl5b77rtPypcv76l4RB67cOGCzJ07V3bv3i0HDx6UQ4cOCQYu+CcPD5pixYpJo0aNBHFSMauYUtK7d2/9PpOaVfZHL/QXXhX58uWTQoUKSZYsWdwaP1FnmTJl1FvT1I/rxZ3AgF6yZElBKAUIWKH+3377TT87P8QULFhQ/8Eab9Zgtq+Nu/xh++SPAZ63689cs+nTp3cY7/29/l0uPf3I64/Xny/XHy6WQO6/+H8Gbyu8nA2gvP54/YXi+sP/f3fiy/Vn7r2YNHUe8/D+69/4K7n8zXmB3H+S+v5z5Mih1WfKlCno40/TD+etL9efKZ+c/h84cMCc7nYb7PbRqBl/u1Mg0Pb//vvvRNUmNf5PVDB+R6Dtu6uT7bt//nLHyht/PBPDkQpGTV+v/5R8NnbXR3f78GwPRzGE+jOOYu7KmX2NGzfW8uazv1tMhHz88ccyf/58wXvzP83fegIpj+fxsWPH6grhNWvWyJNPPhlQnwLRheeSgDsCqeIfxANf1/z/mjF706xZM/2Ru/PUc6dANO778ccfZeLEiTJjxgx18/fGAMbkdu3ayaBBg/Sfgrfy4Xy8WrVqsmnTJmnTpo32P5i6YoYfy5ySmo0NZtusmwQCIYBloj///LMa8uvUqRNIVTyXBEJOYM6cOfoQk5R3fcgVYoMk4COBzZs3yx9//CGVK1fWSWgfT2MxGxD4/PPPpXPnztKpUyfB+0gTPFPce++9OuY1Hq6R1EcYc2AYg+MMPLUpkUVg3rx5cuXKFWnRooVg8t8Xgc3hhx9+kIULF0rTpk19OSXFyrz11lu6WhYKwPEpJibGoQuW/O/fv1+GDh2q+8AiuYKJZzxDwIkKHJ1XUia3Tn/Pg2kJryFDhsgbb7whffv2pQHUX4hJlMf/rylTpkjPnj3lww8/TKIUd3sjkNpbAX+O16tXT5dKFyhQwOfTjh496tOMiM8VhnFBLL+Bt+vtt98ukydP9sn4ie7AK3TChAnqJfvEE0/og2UYdzNsVDOeR3aYIQwbaFQkLAiYa9dsw0IpKkECPhIw163Z+ngai5FAihMw16zZprhCVIAEfCRgrlnEDaeQgN0ImOvXbEOtP0JEvPbaa9KnTx8ZNWqUwD5hpTivdMWydIQKMi+s+sRK2k8//TRgmwieeeFti7AtJh+Llf3wpS7cg/A90oHDF1oskxIELFkC76z4HXfc4fzR63t4jU6bNk2+/vrrBEvlvJ5oswKxsbHSsGFD2bFjh2qO+DC33nqrzmRiaRWW5OCFmyHc1TFjgxeMnzgH8VDgRg9rP27S33//vQaKthmGkKqLmWJ4z+bPnz+k7bIxEgiUAAZDlSpV0lAYgdbF80kg1AQwkMcyNk4+hZo82wuUALzLsKzV2Tsn0Dp5PgmEgkDWrFm1GVy/FBKwGwGEe8PSaXc5IYLdl/fee0/69++vYXtMWy+88IJ62rVt29bsCmjrizEShsvBgwcH1I7zySllTDY6pHT7Rg9uScCVgOUGUNcGvH1GDEZkkYcLOwIfR6p069ZNXd4RB6N79+5q3PC3r4cPH5bXX39d3n//fY2t8fTTT/tbRVSVh0E5pYM/RxVwdtYyApg9LV68uGX1sSISCCUBk3QulG2yLRKwggCSHyGmOIUE7EbAeH76YmixW9+ob+QTwMR/SsiiRYt0ibZr24hJ+uCDD8ovv/wi5cqVcz1s+WcsgcfkBVaJugqOIccKEgmlS5fO9bB+xjL6devWaegsk3DI3BPcneCtTkxiI4wcnkUQuxthJxBSzgjaQ6gNJDiCdysY0eBp6HAb7gQsXQJvOnvkyBF588035YEHHtAfcv369cXdCz8kLAmHzJo1y5wecdvff/9dFixYIEuWLBHMMsGzKzmCmaExY8YIYoP8+9//ThCgPzn18RwSIAESIAESIAESIAESIAESIAESiDYCiM2ZlCAmKZ7bQyHjx4/XpEXObcF2AOcwLM3v0qWLGjc/+eQT5yJqC4DXaOvWrWXfvn0a3/iuu+5So2WCgv//4K3OX3/9VZ599lkpWrSovPPOOxraEPYH5PCYNGmS1oJ4w1jFisRrYPTQQw8JVgDDKEohATsQsNwDFLMFDRo00CDy/gC47bbb/Cluq7LIgNajRw/lYoXiCPqMmTLc6OipYAVR1kECJEACJEACJEACJEACJEACJBAtBLZs2eKxq96Oezw5iYOwCxhvSSz73759u4a4u/vuux1nfPvtt/LBBx/I3Llz1esTGeRhX8EqUoR2a9WqlZZ9/PHH1Ut1xYoVjuRRq1atEuRlcfWq9aVOtPPbb78JVp1u2LBBvVJhJIYuMIoiNioMnki41rFjR9UBqy2R/BoG0t69ezv6wDckEK4ELDeAwqMTGTSxBC5XrlwagHfv3r2JliIjOxgy+sFNGz9e4wkarqAC0QvJj2655ZZAqkh0bq1ateTgwYM0gCYiwx0kQAIkQAIkQAIkQAIkQAIkQAIkkDSB7Nmzq7EvqRLIJWG1wKBolqfDAIpcH3///bejmUuXLgnC3MH71Cx5R2zUdu3aCYynCIcHAyjeI3HSq6++6jB+opK6deuKiQlsKvW1TsRihbfpnDlzdNk9vE+hKwyvENgeoJOzAxYMo5DNmzfrln9IINwJWGoANTEili5dKs4enYih8corrwiS0jgLvEU7d+4snTp1iuhkCYUKFZJly5Y5dz2g9zAe4yZTuXLlgOrhySRAAiRAAiRAAiRAAiRAAiRAAiQQbQTgdYlQdUkJlpZbLf/5z38cBlDUjef6Fi1aOJpBXhQ4k40dO1Y++ugjx37EJXV2qBo9erQea9KkiaOMeQPDLoyrRnytE+WRQR4Cj1NjqNUd8X9g04BjV/r06XXXqVOndNk9PiCuKIUE7EDAUgMoAgW3adMmgfETEDBrgB8pkvc4C7Kf16lTR5eHT5061flQRL2HtyZmU+68807lE2jnXn75Zc2SlyNHjkCriujz8Y8C8WiLFSsW0Qb2iP4So7RzGAwhxAU86V1ncaMUCbttIwIYHCOAPuJGUUjATgQuXrwohw4d0nEDk8nY6Zujrhg3QHDvpZCA3QggmQ7iScbExIRU9eeee06wNHz37t2J2sUy8q5duybab/UO42GJ5L0QY5CFB6ir85hz29u2bdOl9PDadCfOxktf63RXj+u+DBkyKK9Ro0bpM0rbtm21iLkHuZbnZxIINwKWJkG6ceOGY9bAuaOYmVi5cqWcPHnSebe+RzzLadOmyfr16xMdi5QdcBPHDBJuEPfff78sXrzY7wHK9evXBbM3CIYMAyjCBlA8E0DGuq1bt6oR1HNJHiWB8CIQGxsriDuEODwUErAbAcSNQjZS/N+ikICdCCA0Ex4qEf+MQgJ2InDmzBlVF6vxKCRgNwLIKA5bAGJQhlJy5sypNoqHH37Y4dWYJUsW6dOnjyYwxtLzUAiWt1epUkWbMr9hxAb1JJisg+0FGeK9ia91eqsHxxEPFCt9YRzGcnzn5fC+nM8yJJDSBCz1AC1evLhg+burYAYCHpD4cSP7mIlngXIwUkGmTJkiNWrU0PeR+GfcuHFqjIPbO15wTYcbe6lSpdTTIHPmzIKZn4wZM6pxFDcqeDDi5oZZGxhD/vrrL0Xz/PPPS8+ePSMRk6V9wj8FiNlaWjkrI4EgEjDXrNkGsSlWTQKWEzDXLbZp0qSxvH5WSALBIuB87QarDdZLAsEgYLyvzDYYbbBOEggWgZS89+bLl0/tEBMmTNBnbSQPSskVAKVLl1bMX3/9tTpQuTLfs2ePessi+RCcy1avXi3OCZRMeWcPUF/rLF++vDnd7RbOWEh09Oabb0a03cZt57kzYghYagDFcjcE8W3cuLHccccdghmUbt266RZZ0PFjQbKjwYMH64/m559/luHDhyvMSI8bgZvpkiVLdLn/999/r5yQpQ0vXwXGUQRF/ve//+3rKSxHAiRAAiRAAiRAAiRAAiRAAiRAAiSQBAE4aCHDejDEOTSFt8nhpk2bqlMUwgMiVwo+G4G3J5Ieffzxxxo3FKtt3n33XU2KZAyeWH2DcC7Oq3B8rdO0k9R25syZesh5ogU5XSDGiK0f+IcEwpiApQZQ/PCQLezee+91eIIiAzzif8J4hx8s3KVnz56tMSucfyhly5YNY0zWqFagQAGZNWuWzJ8/XyZPniwwhJ49e9Zr5YgDiNmWJ554QmMCej2BBZSAiaVitsRCAnYhgPslhNeuXb4x6ulMANctMo7S+9OZCt/bgYC555qtHXSmjiQAAiYpCe+7vB7sSAD3XBjVUtLzMpjckD3dCAyGxeLzUyQlSELUv39/GTFihOYPeeyxx6R27dqC8ELz5s1z2FEGDhwo8Fr98ccf5dFHH5UhQ4ZoTM63335bEBID4QTgNIUVuOXKlfOpTuiE1acQhJFzlUqVKumuMWPGCN7D8W3u3Ln6vSGbPcIY4BkGq1yRywBi6tMP/EMCYUDAUgMo+oM4l0h2hB8ufnj169d3dBPL4OGq/cwzzySYJYCBD56N0SLI9IYXHhAXLVokiDmFZD1Hjx7VGxYMpUWKFNEXEkXVrFlTbybRwseqfsKojn8wCNZMIQE7EciWLZveI5zDhdhJf+oa3QSQORQPMqlTWxpmPLqhsvchIYBlghh/cdwQEtxsxEICZuIUq+8oJGA3Akg4hHFDpBnwYfyDh+bnn3/u+EpgKzErYpPqr1khO3LkSBk/fry+8P8JBk8YMyFIkorVtA899JBMmjRJJk6cqPE4seL2m2++0XIIT4jM7RBf6oRh88MPP9TyWFqPkHswwJowhR06dFAHLhg9sb9jx46qG2KlfvbZZ4IwfejrG2+84ahn4cKF8tJLL6kjF8INUEggpQmkir/Z/C9toMWaYOYBL/Ojc64ecUDhCbl582adIRg2bFjIs7456xOO75EJD5l0MaMLA3GkSLVq1WTTpk3Spk0bmTFjRqR0i/0gARIgARIgARIgARIIUwJ4KMdy0k6dOiUwRoSpun6rhTBbSDqLMGTwCIs0gbMIMmLDCGTyR0RaH9kf/wggkTJiUsLA5rxM3L9awrs07AHwxMyVK5cULVpUzDJ3V62xNB52F0zgQU6dOiVI8OROfK3T3blmH/KSoH5nfZC/BDlNKMEjgP9fyJsDw7QxVAevtcit2XIPUIMKHkx4uRNkMseLkjQBGAnh7l6rVi2d3Um6JI+QAAmQAAmQAAlU99PhAABAAElEQVSQAAmQAAmQAAmQAAlECgGsBIPzkDfJkSOH4GUkKeMnjvtap6nL3RYGWVeh8dOVCD+HK4GwWJ+GQL2ffPJJuDKiXiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAjYlEBYGUCzVcA4ObFOWVJsESIAESIAESIAESIAESIAESIAESIAESIAESCDMCARlCTzCim7cuFHWrFkjiBGBTOfXr1932/Xdu3cLYoIOGjTI7XHuJAESIAESIAESIAESIAESIAESIAESIAESIAESIIHkErDcAIoAvMgQNmfOnOTqxPNIwBICR44cEQRNr169OoMyW0KUlYSKwOXLl2XdunUazNwENA9V22yHBAIlsGXLFsE1bLKGBlofzyeBUBE4duyY7NixQ2OuMZt2qKizHSsIXL16VatBWDEKCdiNwLZt2wRJdGrWrJkgsY7d+kF9SYAEwp+A5UvgH3vsMRo/w/97jwoNYQBFFryTJ09GRX/ZycghgGyOuG4PHToUOZ1iT6KGwIEDB/TaNQ/kUdNxdtT2BMy4ITY21vZ9YQeiiwCMRxBMPlFIwG4EMG7A/RcZyikkQAIkEEwClhpAsfQd8TwhDz/8sCxbtkwOHz4seAhK6oXZ9n79+gWzj6ybBEiABEiABEiABEiABEiABEiABEiABEiABEggSglYugQe8T7htVS5cmWZPHmyTy7s+fLlk2HDhsmYMWOi9Ctw3+1s2bJJo0aNpEKFCu4LcC8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIBXApYaQHPnzi25cuXSmIupUqXy2rgpkDNnTsHSeco/BMqXLy9Lliz5Zwff+U2gYMGCGk8G1yWFBOxEIEeOHJInTx6JiYmxk9rUlQSUQNGiRXUZZtq0aUmEBGxFAOMGJO7E/ZdCAnYikDlzZlU3Q4YMdlKbupKAEsC4AWEc0qVLRyIkQAIkEFQClhpAoWnnzp1l3rx5fiudVJZ4vyviCSTwfwL58+cXvCgkYDcC6dOnl3r16tlNbepLAkqgYsWKJEECtiSAVUl4UUjAbgTMhBMNoHb75qgvCITLikfEIKURltckCUQ2AUtjgALVoEGDBJng16xZ4zM5zPhMnDjR5/IsSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKBEoD9omXLlhJsp6yLFy8Kkp06C5L2UkiABEJDwHID6M033yyzZ8+Wrl27agIkT93ADWbTpk3St29fuXHjhqeiPEYCJEACJEACJEACJEACJEACJEACJEAClhJ45ZVXZPHixTJ+/HhL6zWVrVixQqpWrarhAhs0aCBYLfPEE0/Ic889Jy+//LIpxi0JkECQCVhuAIW+iAOKV/HixSVLlixJvjJlyiTVqlWTCRMmBLmbrJ4ESIAESIAESIAESIAESIAESIAESIAE/iGwc+dOR0LmF198Uaz2yNywYYM0btxYSpYsqQ5iW7ZskZUrVwpi977xxhtencb+0ZTvSIAEAiVguQF0wYIFUrp0af1RI44Glrcn9cJxCgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmEmsDTTz8tV69e1WZPnjwpL730kqUqDB06VK5duybDhg0TJH+GZM+eXUaOHCnPPvusWwPoH3/8IePGjUu2HoGen+yGeSIJhDkBy5Mg9erVy3EDueWWW6Rs2bKSNWtWcReUG4bRI0eOyPLly8McE9WzI4G4uDi5dOmSZMyY0Y7qU+coJ4BrF4HYU6e2fJ4qysmy+8EmgEE+wtowkUCwSbN+qwlw3GA1UdYXagK4hikkYDcCKTlugPPWnDlzEiD74IMPpGfPnlK+fPkE+5P7ASH/IMePH09UBYyiderUSbAfTmL333+/tGrVKsF+Xz8Eer6v7bAcCdiRgKUGUAT03bdvnxo7p02bJm3atPGJyYwZM+SXX37xqSwLkYCvBLZv3y5Y0lC3bl3Jmzevr6exHAmkOAEEYl+yZIkULVpUqlSpkuL6UAES8IcAJjUvX74szZs3pwHfH3Asm+IEMGbA2KF27dqCmPYUErALgQsXLqiq586ds4vK1JMEHASwHByOURg3pEmTxrE/2G9geO3fv3+iZsz++fPnJzqWnB0IDXj06FHB8nqM63Pnzu2oBiEBn3zyScdnjJ8eeOABWbt2rS6bP3HihE4ow2PUyI4dO/R/FUINmrii5pgv56Ps/v37NRlThQoVOGFt4HEbFQQsdS3Kli2bunMjxoWvxk9QvuuuuxLcCKKCPDsZdALIsgcx26A3yAZIwCIC5po1W4uqZTUkEBICuG7hfRDsTKoh6QwbiSoC5p5rtlHVeXbW1gRMWDHed239NUat8jDgYwk6DI+hlPfff19+//13t03CMxSJna2QHj16aDWrVq2SGjVqyJo1axJU2717d8fnSZMmye7du/XzzJkzNbH0mDFj9DOMxPfcc488//zzmmPlu+++05wrcCYz4ul8lEG/YGh+7bXXpEuXLpIvXz755JNPzOnckkDEE7DUAxRLNeFthyXv/giWyfXp08efU1iWBEiABEiABEiABEiABEiABEiABEiABPwiEBsb6zXWJ2KDNmvWLGAPSXh47t27V0aPHq2el7CXPP744/L666+r85iz4tiPCQ3YRrp27aoxQs3xIUOGyKxZswQeoGXKlJHbb79djaU4p23btlrM0/nffvutYHn/3LlztU8wOiMjPQyw+fPnT/aSe6MftyRgBwKWeoCiw4iXsWjRIo3/5Q+AhQsX+lOcZUnAKwETOzGUSym8KsUCJOADAXPtmq0Pp7AICYQNAXPdmm3YKEZFSMALAXPNmq2X4jxMAmFDwFyzqVKlChudqAgJ+ErAXL9m6+t5gZRDYiKE7/Mku3btcmSH91TO2zH0691335X//Oc/amhEnHQkOLr11ls15FVS57v+nlEPHM2cQ7shXBaMuYcOHUpUjfP5yC0Ag26/fv0cBt20adNKu3bt9DwYYykkEA0ELDeAtm7dWlq2bKkxLnwFiB/t6tWrfS3OciTgEwHMjOEfC2a0KCRgJwJ58uSRSpUqCeLyUEjAbgSwvOtf//pXSON42Y0R9Q1PAqVLl5aKFStKTExMeCpIrUggCQJm9V3mzJmTKMHdJBC+BKpXr65Lw2GQC4Vs2bJFPv74Y5+aGj58uNvkRT6d7FIIxkbEmYaXJoyTf/75pzRt2lSQO8UXGTVqlBw7dsyRSX7FihWyYcMGPRUxPT3JDz/8IMgMP3bsWLn77rsdr3nz5gkSV1NIIFoIWLoEHu7aSNxx3333qSs1MhEWLlzYI0skTULmNTP74LEwD5KAHwQQVLpEiRJ+nMGiJBAeBDAoKl68eHgoQy1IwE8Czp4Jfp7K4iSQogQyZswoJUuWTFEd2DgJJIeA8fS66SZLH+2SowrPIQG/CWDiP5QCL0hf4+UiMSlibvpqMPXWDyQzGj9+vLRv3146dOggSHKEJfL47Pr7Nb9r5zphJJ4wYYIsW7ZMWrRoIdWqVZP169cL7C6u4ny+iXX63nvv6fJ517L8TALRQsDS/5L4QSKGBGYzIK+++qrPHGkA9RkVC5IACZAACZAACZAACZAACZAACZAACfhBAEvFK1eurC9fT0M4NSRqgnONv4LQgHXq1BFX7+w77rhDY4LCCPrXX3+pExm8QZ3F2YCJ/cePH9eYpFhlAyMo9Fq5cqXzKQneO5+PBEoQeKBilSSFBKKVgKUGUPzIHnnkERkxYkS08mS/SYAESIAESIAESIAESIAESIAESIAEwowAQpxgKXmoBJ6XWKr+0ksvJWoSq2axagZeoIg36s0Aeu+99+oy9rVr1/oUZsjZAIoQL5Cvv/5aELLQVfbs2aPJl8qXL+96iJ9JIKIIWGoABZlOnTqpAbR3797qlp0jRw6PwDCTgWxkFBIgARIgARIgARIgARIgARIgARIgARKIBAIIqzJ48GCN+1mwYMEEXYJXqfHMhFenkQwZMuhbLL83AiMpvD1z586dYPm+WXmLxEpG3J0P4yo8WKdOnSqdO3dOYGxFMiis3LVqmb/Rg1sSCEcClhtAy5YtKw0bNtRMZ65xLJICUL9+ffnwww+TOsz9JEACJEACJEACJEACJEACJEACJEACJGAbAqVKlVIjJ+wj3377rSY5NcojIRGMoF26dNEkUGa/iUU9ffp06dixo6xZs0aaN28uhQoVkoMHD8qgQYM0ZigSGx04cEBPw1L7a9euSZMmTRyxrJ3PR5Lq/v37q6PanXfeKY899pjUrl1bkyghEdLs2bMFWeYpJBDpBIJylb/22ms+uWUbuPny5RP8ECkkYCUB/EOBO7+vQa6tbJt1kUAgBBDIfO/evXL27NlAquG5JJAiBLCy4/DhwynSNhslgUAIXLx4UXbv3q0PkYHUw3NJINQETAIUGEAoJGA3AvBuRGzOSBQkNc2fP796XCLeJxy/kAUeRk54XQ4bNiyRI1jdunWlZs2asnXrVt3C6xOJokaOHKlGUDiOYUl9s2bNNBYojo0bN06Xx4Ohu/Nz5swpyGiPhE6IHYpETF27dpUFCxZoHeXKlYtE/OwTCSQiYLkHKFrAj85f8ZYt3t/6WJ4Edu7cqf8I0qdPr/8sSIQE7EIgNjZWtmzZogOmWrVq2UVt6kkCSmDDhg0aR6pVq1Z+TYYSHwmkNAHEYNu3b5+kS5dOihQpktLqsH0S8JmAWSprltP6fCILkkAYENi4caNcunRJ4BSFpMqRJOjP8uXLxcTgPHXqlGzbtk0zvyMZkVmu7txnPLvC6xPOEEWLFnVkh3/wwQfV8/PcuXOCbPJG4BWKlbcwbEKSOh8xQf/973/Liy++qMbVXLlyaf3OsUJNndySQKQSCIoHqL+w/v77b/noo4/8PY3lScAjARMLxWw9FuZBEggjAuaaNdswUo2qkIBXAua6NVuvJ7AACYQJAXPNmm2YqEU1SMArAeMBarZeT2ABEggjAuaea7ZhpJolqhjjJyqDJya8QCtVquTW+GkahFESXqKuIQVh5HQ2fqI8DJ7G+OntfBzHJF+1atWkWLFiQuOnIcZttBBIcQMo/lHD+Ml/2NFyybGfJEACJEACJEACJEACJEACJEACJEACJEACJBA6AslaAg+36aVLl8rQoUMTZRCrV6+ezzEXr169KljqiWUbiH9BIQErCSDTHcRsraybdZFAMAlkzJhRq+e1G0zKrDtYBHDdYimbqzdCsNpjvSRgFQFzzzVbq+plPSQQbALwAIPwvhts0qw/GARwz4UzlKu3YzDaYp0kQALRTcBvA+ivv/6qAXSBbcCAAYLPRnLkyCEFCxYUZCGjkEBKEyhbtqy69ruLrZLSurF9EvBEIFu2bNKiRQtdouKpHI+RQDgSaNCggT7IMJtoOH471MkTASxTROxPjhs8UeKxcCRgJk6zZMkSjupRJxLwSAAOVDCA0oDvERMPkgAJWEDAbwMoMpkh7gTidlapUiWRCo888ogaQPGPGMZQT4NIeIAi6xuCAVNIwGoCiGni6fqzuj3WRwJWEjDeHFbWybpIIBQE6MERCspsIxgEOG4IBlXWGUoCjOcXStpsyyoCHDdYRZL1kAAJeCPgtwE0a9asmpFs8+bNcttttyWq/5577tEMbsjmFhMTk+i4ux0jR44UZi10R4b7SIAESIAESIAESIAESIAESIAESIAESIAESIAEAiGQrCRIuXLlkiZNmrh1U8fSzS5duvhs/ITyXbt2DaQPPJcESIAESIAESIAESIAESIAESIAESIAESIAESIAE3BJIlgHUbU1OO/v06eP0yfvbPHnySLt27bwXZAkSIAESIAESIAESIAESIAESIAESIAESIAESIAES8IOA5QbQ/fv3S968eX1WoW/fvpoFvmLFij6fw4IkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4AsByw2gderUkU2bNvnStpbp1KmTtG3bVq5cueLzOSxIAr4QOHLkiCxfvpzxZX2BxTJhReDy5cuyYsUKOXDgQFjpRWVIwBcCW7ZskfXr1/tSlGVIIKwIHDt2TMcN586dCyu9qIx/BLp166YJWxGWy7x69OihlUybNs2xzxyrVKmS7ceKSCwLuXjxom75hwTsRGDbtm2ydu1azQRvJ70jWdcbN27Izz//LNeuXYvkbrJvUUjAcgOovwyrV68uK1eulHHjxvl7KsuTgEcCMICeOnVKTp486bEcD5JAuBE4ffq0XreHDh0KN9WoDwl4JQDDPa5d80Du9QQWIIEwIWDGDbGxsWGiEdVIDoGjR4/q6rKzZ8+KeV26dEmrwsO82We2MHzb/SHfJJPFBCqFBOxGAOMG3H8jzSEKY6Fnn31WYO/ARAtejRs3luHDh4ftZAWMno899pjkz59f4NiG+ySFBCKJgN9Z4K3sPIxT77//vuCf9eLFiwXL4SkkQAIkQAIkQAIkQAIkQAIkkBwCc+bMcfvQfv36dbcJXDNmzCg33ZSij0TJ6SbPIQESCHMCMTEx8uabb8pbb70lAwcOVG3nzZsnLVq0CFvNa9WqJYUKFZIZM2aErY5UjAQCIRDwf/sHHnhApk+f7tABgwtkiDcDiVSpUom7F2ZizWwlTs6ePbujDr4hARIgARIgARIgARIgARIggeQQyJo1a3JO4zkkQAJRQuC7776TdevWee1t8+bNpWHDhl7LeSpQvnx5x+HbbrvN8T4c38BuAwNokSJF5K+//gpHFakTCQREIGAD6FdffSWjR4/WWQ2z3M3f+DOZM2eWRx99NKCO8GQScCVQsGBBNbLnzp3b9RA/k0BYE8iRI4fkyZNHMHNMIQG7EShatKiu7EibNq3dVKe+UU4A4wYs98P9l0ICdiKAZylIhgwZ7KQ2dSUBJYBxAxyj0qVLFzIi8BT/5JNPvLaHyZRADaDGMQyN2eU3mjp1ikdK9PrdsAAJJIdAwAZQNNqvXz+pUaOGtGrVShC7rmrVqhpg3JNC+FEVLlxYSpcuLffdd584z4x4Oo/HSMBXAohdgheFBOxGIH369FKvXj27qU19SUAJVKxYkSRIwJYE8uXLJ3hRSMBuBMyEk12MK3bjS32DS6BChQrBbcDmtSNs4K5du6R48eKSN29ej71BDOTt27dLtWrVkrTH7NixQ8tkyZJF7Ta5cuXyWCcPkkAkEbDEAAogeFifPXu2NGvWTD744AOpXbt2JHFiX0iABEiABEiABEiABEiABEiABEiABEgg6AS2bNkiPXr0UKcxGIkRlxO5U8aPH5/IK3Xnzp3Su3dv3X/zzTfLE088oSvJPvzwQylVqpTqCi/bDh06CCZMevXqJQgDcO+998rEiROlbdu2Qe8PGyCBcCBgmQEUnYERdObMmVKgQIFw6Bt1IAESIAESIAESIAESIAESIAESIAESIAHbEFi7dq0aM+FYZkIFDho0SPc1atRIvv32W2nTpo32B4bS22+/XX766ScpW7as7oNXZ/v27aVz586ycuVK3TdkyBCZNWuWwAO0TJkyes7u3bvl8ccfpwHUNlcGFQ2UgOXBHe644w5BHA9vcuzYMblx44a3YjxOAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhFPAEml4fkJT86uXbs6+osQF+PGjZO4uDj18ETMagg8P5GY2hg/se+ee+5RA2fNmjXxUQUhCBHT1HkZPew2sbGxcujQIVOMWxKIaAKWG0ANLfxwL1y4ICYxktk/efJkQZB5xGZEoo/u3buLv0mTTF3ckgAJkAAJkAAJkAAJkAAJkAAJkAAJkEAkENi6dats3rxZvTSRld1ZENsTuVOOHDkiq1atkk2bNqnnp2v4QSxzX7Rokbz77ruO00eNGiVwQsuZM6fuW7FihWzYsEHf79+/31GOb0ggkglYbgCtXr26pEmTRrO43X333YIfsJH33ntPHnnkEf3BYh9mLZB97a677jJFuCUBywhgdozGdctwsqIQE7h06RK95EPMnM1ZQ+DatWty5coVaypjLSQQQgIcN4QQNpsKCgFcwxQSsBsBjhsSfmPGfpJUUj4YQSHbtm1z2FrgWOaLwDA6YcIEXRr/559/arIknMd7hy/0WCYSCFhuAM2cObPUr19fDhw4IIsXL9bMYgD166+/yoABAxzMEGti9erVOiuxd+9ejR3qOMg3JGABAWTAW7hwoZw4ccKC2lgFCYSOwJkzZ2TBggV63wxdq2yJBKwhsHz5cv3/zzA31vBkLaEjgCQSGDfAQ4ZCAnYigFV3kHPnztlJbepKAkoAMSrhrYgVpBRRRzJwgJenO8FKWgiMmcgQD0EsT29y/PhxqVGjhvz8889qBH3ooYe0Dm/n8TgJRBIBS5MgweMDP1S4YmfJkiUBp549ezqWwyOWBbKXQeCujR/x3LlzNVZFgpP4gQQCIGC8P802gKp4KgmElIC5Zs02pI2zMRIIkACuW3hz4EEG8aYoJGAXAuaea7Z20Zt6koDxuqcBideCHQmYsHkYO2AlabQK/vf0799f+vXrpwjg4YnftCsTOPlAbrnlFod9ZdKkSdK3b19xXTKPsjExMRr7Exnf//jjD0GCJdc6tUL+IYEoIGCpARQ/sMaNGycyfn733Xfq7Qme+AGOHTs2AVoslf/0008T7OMHEiABEiABEiABEiABEiABEiABEiABErCSQNOmTRPZLNzV75xEyN1xX/b5uiLmlVdeESx7L1eunNSqVUvWrFkj8+fPTxAuEEvVsYoWCY+w6vb8+fMa0xMOaCNGjJAXXnjBodLhw4flo48+knfeeUdXRMLTNnfu3Ak8bbEMHuKrjo7K+YYEbErAUgMoGJglGIYH4nw+88wz5qO88cYbkilTJsdnvDl69KikT58+wT5+IIFACRjPI85wBUqS54eagLl2zTbU7bM9EgiEgLluzTaQunguCYSSgLlmzTaUbbMtEgiEgLlmXb2/AqmT55JAqAiY69dsQ9Hu/fffL3iFQg4ePOhoBkZJOIQZgUETMT+RF2XMmDECIyV+xzBcwgg6aNAgadiwoXpw4hx4byJU1pdffqlL5dOlSydvvvmmJpYeOnSoLF26VBo0aKDJjVB2/fr12lSuXLmkUKFCAl1QZ/v27eWHH37QsIUogBAE8MBt0qSJGkMRzhCC7PAmaZLu4B8SsDkBSw2gmImYM2eOYCahcOHCOrvQo0cP2bNnj2KCd+jDDz+cCNn06dOldOnSifZzBwkEQqBMmTKSLVs2DbEQSD08lwRCTSBPnjxSqVIlwZZCAnYjgPhS0b6MzW7fGfX9HwGMRRHL3vnhlGxIwA4EsmbNqmri+qWQgN0IIIny5cuXIy4eJYyHo0ePFixPN1KqVCnJmzevJh1CzF68MGaCwNAIoycEzwEwXnbo0EFX2CKEIJbDT5kyRX788Uc1cmrB+D+PPfaYerM+9dRTGoMdx+vVq6exPmH0hMAhaOTIkTJw4ED58MMPNYHSyy+/LG3btpVmzZrJuHHjpHjx4rJu3TrN0RIbG6vnYUl+nz59pHXr1vqZf0jA7gQsNYDCi/PJJ5+UEiVKCNzKt2zZorMMgIQH+Y8//jgRL/yIn3vuOfn8888THeMOEgiEADyNcS1SSMBuBDDzi0EIhQTsSAADewoJ2JFAxowZpWTJknZUnTpHOQHj+XnTTZY+2kU5VXY/VAQidcIfk2nwzsQrOYIYn7/88ovG7fztt9/0/xOMkeb37lzngw8+KHjt27dPkBHendcmjsPzE0bX7NmzO06HVyjuHWbV5BdffCF4UUggEglY/l8ScScwY4BZBCOY6Zg2bZpjUIkl7/D6/Oqrr+Snn37SYs4/QnMetyRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQjQSKFi0qePki3hwoYOR0tbswFKEvZFkmUghYbgDFDwhu1YgtsXnzZkG8CbhyO/+wEBcUMz29e/fWF2Ai4C+FBEiABEiABEiABEiABEiABEiABEiABEiABEiABKwkYLkB1ChXrFgxwcudIMYSY366I8N9JEACJEACJEACJEACJEACJOCewLfffiuTJ0/WGIKmxIkTJ/Qtwo+1adPG7NYtwpK8++67Gt82wQF+IAESIAESIIEoIxA0A6gnjlevXo24IMee+stjJEACJEACJEACJEACJEACJBAogU8//VTmzp3rthqEIZs5c2aiY0888YRUrVo10X7uIAESIAESIIFoIpAiBlD8Ay5QoIAmSkKypCpVqrgN5htNXwT7aj2BCxcuyJEjR9QT2QR1tr4V1kgC1hOIi4vTIObw2jCZXa1vhTWSQHAIHD9+XDOaFixYMDgNsFYSCBKBixcvCrL2YgUTk8kECTKrDZjAxIkTZfXq1Qk8QDFu+PnnnzX5580335ygDYwlaPxMgIQfwowAPJivXLkiSBpEIQESIIFgEkiWAXTZsmWye/duvVHhZoVBIuJ5+ipLliyRIUOGyODBgzVWKOKBjho1Sjp27OhrFSxHAl4J7Ny5U7PmIf5soUKFvJZnARIIFwLw4MAytvz582sM5XDRi3qQgC8ENmzYoOODVq1aOTKK+nIey5BAShPYtWuXTj6lS5dOihQpktLqsH0ScEsABs3WrVsnOHby5El1JkFOhTp16iQ4xg8kEO4ENm7cKJcuXdKcIGnTpg13dakfCZCAjQkkywA6duxY+eabb7Tb9evXl/vuu88vBPjH/fHHH0uTJk2kQ4cOmjUeBlUKCVhJ4MaNG1qd2VpZN+sigWASMNes2QazLdZNAlYTMNcttvS+t5ou6wsmAedrN5jtsG4SsJoAr12ribK+UBLg9RtK2myLBKKbQLIMoAYZAnA7e21u2rRJxowZYw4n2o4ePVqyZcvm2P/QQw/JmjVrBPtTpUrl2M83JEACJEACJEACJEACJEACJEACJEACJEACJEACJGAFgdTJraR8+fIJjJ+op1SpUtKyZUs5c+aMID4NXl988YVkyJBBHnjgAbfZBx9++OHkqsDzSMAjgUyZMulxs/VYmAdJIIwIZMyYUbXhtRtGXwpV8ZkArlssIab3p8/IWDBMCJh7rtmGiVpUgwS8EsC4Ac4kvHa9omKBMCSA6xZL3xl7OQy/HKpEAhFGINkeoLfeemsiFEjW0b59e31169ZNJkyYIO+++64g82BSUr16dcmSJUtSh7mfBJJNoGzZsprIAAZ4CgnYiQA85Vu0aKFGJDvpTV1JAAQaNGigyTlSp072HCtBkkCKEChdurTG/uS4IUXws9EACOBZqnnz5mpECqAankoCKUKgXr16Om7gxGmK4GejJBBVBJL9dJIrVy6PoPBPGIKHeE+CByQE7KaQgNUEMBPOhxirqbK+UBFA8i6GBgkVbbZjJQF4cDCJgZVEWVeoCHDcECrSbCcYBDBu4MRTMMiyzmAT4Lgh2IRZf6AETpw4Ib/99lug1fD8MCCQbA/QzJkze1TfGDXN1lPhHDlyeDrMYyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgCwJ///235jpZunSpJn2G0jD2NmrUSMMD1qpVyxb9iFYlL1++LEj+PX36dM1b8+STT+r3mRSPuXPnyqxZs2T16tXq0YxyRYsWFXg4P/fcc0mdxv0hJpBsA6g3PY33hy+xPLBsIy4uzluVPE4CJEACJEACJEACJEACJEACJEACJEACARO4fv26zJ8/X5YtWyYnT56UAgUKSNOmTeW2224LuO7s2bPLiy++KEj8XKZMGa3vpZdekmHDhgVcNysIPgHEs+/fv7/A+xNGTW+CXDh4IcfN1KlTNczjqlWrGO7RG7gQHw+aAdSffly7ds2f4rYr+/vvvwuSRvki69atk/Hjx8vOnTv1BU/bW265RRArFbMOuXPn9qUaliEBEiABEiABEiABEiABEiABEiABEnBDYNOmTZrU2XVp84gRI9RrD8mc4cEXqCC+NJzDrl69qs/1gdT3xx9/yLx586Rnz56BVMNzfSCAsDh41alTx4fS/xSpUKGCfsC1w1w3/3AJl3dhYQDFzSCSZfDgwfLdd9957OKlS5fkqaeeko8//lhu3LiRoOzevXvl+++/V5froUOHSt++fZldNwEhfiABEiABEiABEiABEiABEiABEiAB7wQ2b96sSRvPnz8vpUqVkk6dOkmhQoVk165dMnHiRFm5cqXUrVtX1q5dKzExMd4r9FICq2Jh8zCrZL0Ud3v4ypUrcv/990urVq3cHufO4BDwN7ay+Y7NNjhasdbkEkh2EiTERLBK4FYcyQLj5rZt2zx28YUXXlDPT1fjJ1yvTYzUU6dOydNPPy0DBgzwWBcP/o/AkSNHZPny5YJ/bBQSsBMB3F9XrFghBw4csJPa1JUElMCWLVtk/fr1pEECtiNw7NgxHTecO3fOdrpT4egmAMMIxg379++PbhDsvS0J4DkZhsZQhcTD8zYMnnhG7NChgya3wVL1Rx99VF577TXZsWOHxuk8fPhwSDwtYRiFw5MRrB51/oz9eDaA8ROcoDfsJ4gx6iq4B8C4i3uCO8HK23379ukh1AMvWGfZvXu3wxkL/xMXLVokf/31l3ORRO8DbfPo0aOycOFCAe8LFy4kqt/sgC0E/fdkO/KFpakPW/y/h70AcT7hCexqi3Euy/eRQSDZHqAzZ86UggULSqZMmdySMD9aBI5NyvqNmxyCxeJHE8nZjsuVK6f9xFJ2d7JhwwYZNWqUHsLsEFza4WpdqVIlKVu2rPLDzWDNmjU6IzVmzBipXLmydO3a1V113Pd/AjCA4kaJeC7eknYRGgmEE4HTp0/rdZsmTRopUqRIOKlGXUjAKwEY7jHAxv+ppP7/e62EBUggBQiYcUNsbCyXraUAfzaZfAIwhGC8i+epYsWKJb8inkkCKUAA4wYY7PBKnz590DVYvHixbN26VYoXLy6fffZZorFKtmzZ5Ouvv5aSJUvK7NmzZc+ePfreasV++uknXf2JlZ5t27aV7t276/M9DLCQHj16yLhx4/T9pEmTBMZJCOww0B9JlLA6FLJgwQJ555139PcPmwHsK2+99ZY89thjevzXX3+VKVOmCJb133vvvXLHHXfII488ImfOnNFEP7A1oM+YwEbdTzzxhEA/2GswloNN5/HHH9e6zJ9A2oSXLWKtIsTf7bffrjYPvMd342rYxcQ6WBQuXFiwvHzGjBlqEEbYwIYNG6o6/rA0+qMeOJcNHz5cV9ciVivCDSKREZeuG0qRt022AfTPP/+UIUOGeCUycOBAr2UivUCzZs30JoIfLm6oroIZD8w2IE4EAubC3d5VYGhu3LixvpA5DgGUaQB1pcTPJEACJEACJEACJEACJEACJEACJOCeADz+IPD+xGpLd5I3b16566675Msvv1RDIIyhVgsmimHUg+MDdMKqTyRkggNPx44ddXVoly5dpHbt2mp8hIG4T58+agN49tlnHep8++238sEHH6jDFfoDL8gGDRqoQTV//vy6ZB774OEIL0s4X8E5CAZSnIe+5cmTx2FMRd0vv/yyGhthMB00aJAaIOHMhYzmkEDbhN0D7UCP3r17a52wdSDvCewiZtk5PD5h5ISe8NCFQB/sg00EerRp00Yn3X1liTrgdQqDJ4zBYA0pUaKEsoax2eikB/gnogikjqjehGln8OPETAayiLmTX375RXfjx+bO+Ol6DgyfMIjCU4FCAiRAAiRAAiRAAiRAAiRAAiRAAiTgnQAMjBBkfPckWO0K8bYE3FMdno7BMQrGOwjaMh6cVatW1eXu2I/l7K7ivHIWofbgxdivXz+HMRcem+3atdPTXn/9dd3CsAhjKgQGVyz1h8cplsC3aNFCatSoobFQcfztt99W42K+fPnUfmGc3l599VUcFivahH0E8VZhB0F9EKx8g8cqjLWQ69evq+H15ptvTuD4lSFDBvWMhXcqPFXPnj2rTmb+sMRKJRiLnQ3bJuGVO+aqEP9EBIFke4Ci97DY48aBizA5grgTMOKZiz45ddjhHBgrkbjolVde0ZsJYo44C1ysc+bM6ZhRcT6W1HtklccNw9uNO6nzo2E//pHgGoMrO4UE7EQAM8CYibUi6Lqd+k1dI4MABpCIVcXl75HxfUZTLzBuwIMU7r8UErATgezZs+t1iyQuFBKwGwGMG/DMlpQ3ptX9Mc/PO3fu9Fi1WYpuDKEeCyfzIMLfQVztKUjMBIGnoif54YcfBJnhsUT9o48+chTFknbX8HswJELgHepsRDUnGV3g/eosvXr1Uo9QLKOHWNVm69atBXlQEPrvk08+Ue9PLIM3gqX4MEZiub6rvtWqVRPYQxAvddWqVdK8eXMx+vvCEvfK48ePO0IuwCj++eefa9MIH0CJXALJNoBilmHkyJEBP+Dgx/nggw9GLuH/9wy83n//fY21sW7dOnU5Nzd5/IAR/Nn8aL3BgFs44lzAbZ2SNAG4/ONFIQG7EUD8I7PExG66U18SqFixIiGQgC0JwNsFLwoJ2I0Anik4brDbt0Z9DQHEdQylwFgGw9vkyZN162rwgy5YLo4Yl3g+R4zKUIsx+LlLDGWOQScYACHvvfeelClTRt9b/QeGUzi+GWOsVW2aOKPgXLNmTencubPaS0yOGRhAIUn9X4YNBbrAjoLvNCkxvFxZwlCKuKrIxZI1a1aNw4o6XMslVS/325NAspfAI2aDFd4duMCjIcZCrly5ZMKECXqV4AaFpe5m6TviXZgZJl8uI9ys8YNFnAoKCZAACZAACZAACZAACZAACZAACZCAdwJY7g2DGbz+EOcTuU2cBYa3u+++WxM6IvGPOwOpc/lQvzcGPbQLz1nI9u3bdRusP4jJaZaIW9UmVsDOmzdP3nzzTbVtIDES4p2aMH/GWcx8du2bcXRKrk0KcUVvu+02XV6PUAHOy+Fd2+LnyCGQLAMoZkGsdAWH9R4ZzyNd4Ob9zDPPaDcRfLhKlSrq8o1MbZi1RQY4T4JQAQMGDND4Ha5Z2Dydx2MkQAIkQAIkQAIkQAIkQAIkQAIkQAIiyL0BZyKszITn5D333KNOWUhejGf0vXv3apZ1rHgNN3E2gJYuXVrVQwZ3d4IM9sZj091xX/YhlwnCw8BmA7GqzY0bN+rSdiTNhjMY7CHI+A7DJMSsKIKHJ+KBuoox+rou9Xct5+4zlvHDCe+pp57S+KfuynBfZBJIlgG0Z8+eltLArErbtm0trTNcK8NNdOjQoQ71fv75Z511mDNnjmYiwzJ5/JhjY2M1Sxsyn02bNk2PgROCI5t4GY5K+IYESIAESIAESIAESIAESIAESIAESMArASzrhhMSMoEjbvmsWbPU8AbDGLwdkW19yZIlmnjYa2U+FEAIO4g7Q567090twzaxLc+cOeM4pWnTpqrj1KlTNTan40D8GyQ7QuKismXLOu/2+/2MGTP0HJN53qo2kWneCDK4L1q0SBDPGN8LpFy5cmqERqzO+fPnm6K6BZ/Vq1dr3+rXr5/gmOsHdyxnzpzpqMeUN57A5rsy+/3dmvN9/a79rZ/lAyOQ7BiggTUb3WcjGRJ+5PAGNTcw/DDhTu4c+NcdJQQJnjJlSqJAwO7Kch8JkAAJkAAJkAAJkAAJkAAJkAAJkEBCAkh4B8Mhsp4vX75cTp48qQmGGzVqpAmKE5ZO/qcTJ06okRU1HDx4MEFFSGAEcc00bxI0HTt2zFHeLNGePn26dOzYUQ2FLVu21EztI0aMkDvvvFOzqGMZOVabYnn57Nmz1aCLSg4dOqR1mdiajopd3iAh0aBBg3QvzoHtAomQEDoAAuNx//79JdA20UesgEW4ASMwPsPACoGnKxI71apVS/Vp2LChxurEMTiJwY7y5ZdfOpJn+cPSrD4eM2aMrkSGl+vcuXM15iu8UdevXy8ZM2bURFL79u1Dkw5++sHDH2NIxdJ9GEGR3Z4SRgTiDW+UFCIQ/6OIi88IHxcftyIu/pLw+IrPVBYXH/szLn5GIYW0tabZqlWraj/btGljTYUeagGr+CRbHkrwEAmEL4GLFy/Gxf/TDF8FqRkJJEHg6tWrcfHeFEkc5W4SCF8CHDeE73dDzbwTiORxQ7yhRJ8f4pfeegfBErYjkJxxQ7yRTK+JhQsXhm1/441qcfHGw7h4w6HjOT937txx8cbDuHgvx7hly5Y5jsUb++LiPSLj4r024+Kzkcfh2R/2gXgjbdy4ceO0j/Hh8OLikwXp/hw5csTFGzh1P/53Pf/883HxMTMd7eC3gvqNjB49Oi7eo1KPo60ePXrExS//N4d1W7lyZT0eH3IvLj5GalzXrl3jUA/qdhUr2ow3aMbFO4XFxXuWxn366adx8atc4+KXpCd6/ok32MbFGyzjqlevHhef7T4OfQGHeKO1Qy1/WcYv6Y9r1apVXLzBNS4mJiYu3uAbh3tot27ddF98KIS4+KRPcfGxQePiY58ql/hESXHDhg2LizdKO9p1fhNvQI2LXykdF284dXwPqAd1WCHxRm+tF21Qkk8gFU4NI3tsVKqCGZ/vvvtO3b4xK4SZlixZsmh8DcQkgft3+/btdRbC7oAQO2TTpk0SbwAV404frD4h3glmlpBwKtyCVwerz6w3MghgRhPLbhBsHHGIKCRgJwI//vijejogwQBm8ikkYBcC8PpAGCJ4z8DDhUICdiGA+Hy492KFmYnTZxfdfdFz165dGqcRsQeNZ5wv57GMPQjEG690JSTGDb56yyFWJ5arxxtAHR6D9uhtYFrCdIP4pHhGQIZ6Z7ly5YrAuxPJl3HcOVaoc7mk3uOZA0maEYoP5yLrO5bPe/pOAmkT5yLREZa4o83ixYt7tHfAw/O/7d0HvFxVnTjwEwEpgdAEQhETmjRBIEhRgXUhC4is67IsCKiIK2BZEHcXRQFxpYgiwqIUFQFdlEWKSFGIYKNJk5IAoUkJSJcAoSQw//mdz/+O7z3eS97Lmzkz8973fD6TKffec8793pv7Zn73lGnTpuXJiuJaMNT962+/Iw4TkzH1zCt65cas952W6g3nck/gGI7y5JNP7rTqdU19ev+v6Zpqj6yKxkXq4x//eH6MrD1r797U7+LkClTP7a2N0gkMXqA6Z6vnwW9pTQLtF4jzds6cObnbjwBo+4+HGgxeoLrmVs+D39KaBNorEBOlRnLutvc4KH3+BOo99lK9FWj+7jC3YNv85T6ytopAXdUVvu+eRTCxWTdAIj4Rj3ml4ZQZ20Zafvnl82NeZUVQNx7NTP3tYycGP5u5z6M9L00zRvsZYP8JECBAgAABAgQIECBAgACBUS+gg/CoPwVGNIAAaJcd3uOPPz5NmDAhRdNnae4CVcsjdxLn7mRp5wlU52713Hk1VCMCAwtU5231PPCalhDoLIHqnK2eO6t2akNgYIHqu65zd2AjSzpXoDpvq+fOrenIrVlM1lNN9tNz4qWRu8f2bLQK6ALfRUe+Pihy+sIXvpBivIxTTz01z/72nve8p4v2oGxVY/zUcePGpfHjx5ctWGkEhikQs1LG7ITxLBHoNoGYJTS6wFc/yLut/uo7egViTLHo+lafEGH0ItjzrhSIMezqE5gMqstqV+6gSo9ogfrkNnns8PrEwCN6Pzt156ZOnZq+8pWv5LEw41py0EEHpRiPNWZ67zk2ZqfWX70IDEVAAHQoWm1eN4J58aU87s7EH4iBxv9oczU7pvjFFlssrbbaah1THxUhMFiB+LIRA4FLBLpRwKRz3XjU1DkE6jO3+m7lVOhKgfjeMKHeQ0wi0I0Cbvi396itt9566dxzz21vJZROoJCAAGgh6GYUE90CYpa8c845J2277bZpxRVXbEa28iBAgAABAgQIECBAgAABAgQIECAwYgUEQLvs0L71rW9N//Ef/9FltVZdAgQIECBAgAABAgQIECBAgAABAu0RMAlSe9yVSoAAAQIECBAgQIAAAQIECBAgQIBAAQEtQAsgd3sRr7/+enr44YdTrVYb9q688sorOY+YIGPWrFnpzW9+c1pwwf5Pwyj35ZdfbpQZY3MNNBDz7NmzUzwixVABiyyySGO7vi9eeumlxr4on7/zz/+/vteIeO/64/rr74+/v9W1wfcP3798/xxT/Xfo9Vzq+3evQnu8KVW+49/e4z+Qf49TwUsCBAgMSqD/X76D2tRKo0Xg3/7t39Lpp5/e1N29++670xVXXJEnc4pZ5vqbLfiaa65JTz/9dKPc6P6/8cYbN95XLyKgOWXKlBywqD7bbLPN8gzwjz32WB4Uvso/JpC67bbbqtVaWn5/s88rn/9gzr+42fDrX/863ySoTtahnv/Ov/EVXePZ/78y///WXnvttMQSS6SVVlqpYR8v+Jfxj79//v/P3///+D4xY8aMFN8dnnnmmcb56/o7tO9fzr/5O/+qE24433/f8Y539DsBaLdff+NvSt800Pd/51/7zr/5/fvz5JNPpldffTVP9jvY8/+1117re0oM6/1DDz2UzjzzzPTv//7vackllxxWXjYmQKBzBQRA23hsogXkpZdemu699970yCOP5C/dM2fOTMsuu2yK2fBiNsdtttkmbbLJJv0GCEtVfYMNNshfpqJF1HDTE088kYM6q666ap5tNb7QRIvN/tJSSy3VCADFnb9x48b1t1puQRp/qKrWOtGiL1prTJ8+PT344INp4YUXTqusskreduzYsSke1b60svz+Kqt8/oM5/5566ql87sf/jTh/5+f8d/69UcD/vzL//+67777cIn+nnXbq9beLfxn/+PvXX+I/b/977rknB+qXW265/D0iHF1/h/79y/n3RoFW//+LYFAEkCKAv9pqq72hAq0uPwqsvn+/ofD6B8Mtv79eaFFef9//W1F+f3kqv3n+N998c/4dt/zyy6fB/v5rdqvQvfbaK/3ud79L999/f/rhD3/Y3yH32SgWiCB9PNZdd91RrDAydn1M/Q/K8Ps1jwyLYntx5ZVXpjPOOCNdcMEF6YUXXphnuRH422WXXdLBBx+c1lprrXmu38krfO5zn0vf/va30/HHH58OPPDAllY1/phG1/2NNtooRcBVItAtAo8//ni67rrrUnwR3GKLLbql2upJIAtccsklKYY52XHHHXMreywEukXgT3/6U75xuuGGG+ab0N1Sb/UkED/Mo+VcNKB497vfPeJA4uZE/AZac801cwOHEbeDo3yHLrvsshzA33777fON/8FwTJ48OfcmvPzyy9N22203mE0GXOfcc89Nu+66a14egdU//vGPadKkSQOuP9gFzz33XDrhhBPSb37zmxSNGyJF4DwaOP3rv/5rihazUucKxNB9J510UjrvvPPS9ddfnz7zmc/k4zlQjaNh20UXXZSuvfbaxnB7b3vb2/I1ef/99x/2uRBB+h//+Mdpv/32SyeffPJA1fD5PAS0AJ0HUDMXR+vHOPnPP//8IWUbrUKjC3oETffdd9904oknDjhu5pAytjIBAgQIECBAgAABAgQIEBiFAtGD8D//8z/znkevxxiq6oADDkhXX331sDWihfJhhx2Wdt9990Yjpq985Svp8MMPH3beMmi9QMwVEo234gZTBDXnleLGfzz22GOPdPbZZ6fFF18835iK50jOhXkJllkuAFrGOd/12WqrrVKMfRkpWiTGOD1xJzPGllpsscXyIybviTFtnn/++fyI4GdsE3eioltLRPuje+EvfvGLPIFQoep3ZTFhGql67sqdUOlRKVB1YXXujsrD3/U7Hedt/KCoxl7u+h2yA6NGoLrmVs+jZsftaNcLVBN1OXe7/lCOyh2I8zY6pUbryNLpG9/4Rm75/853vjO31IxxzKM1dQSwPvzhDzelOvF7f6GFFsrDA6233nrDyjOGd4sWs9EKUGqtQLQGjsdQe+NV3eSj9WcV/Kxq2sxzocrT89AEyl9lhla/EbP2Pvvsk8f6jKbTMalQ3GEaanr00UfTMccck77zne/k5tgHHXTQULMYVeu//e1vz13Y5jYj/KgCsbNdIxDDXkQ3oLjzKBHoNoH3vve9+YfMQOM7d9v+qO/oEYgfJnGD2veG0XPMR8qexo/smFQ0giwSgW4TiGEbIgBa+sZpNC76+te/nrmiq3q02Dz66KPT3nvvnYee++AHP9i0hjQR3J09e/aw/o/GOL/RVT/GWJfKCQz1+2x1Ha6e+9a0GedC3zy9H7xA/7PPDH57aw5C4M4770y/+tWv0lVXXZX+53/+Z76Cn1FMzKgb3d/jrs/Xvva1xkQ+g6jCqFwl7tj4ETMqD/2I2Olq8qMRsTN2YlQJxBe7gb70jSoIO9t1Ar43dN0hU+EeAvG9Yag/1Hts7iWBtgm063tDzK/x4osvpn/5l39J0VMz0kc/+tE8/mdMUBwNj0qkCIzG5EtVithBz/fxeYxHGcHP6BUadY5u2THGaN/05z//OcV41hEs7S/FGO0PPPBAXhT53HLLLb1Wi8mZq8mCY06CKVOmpGeeeabXOn3fDLfMv/zlLynGco3GXjFJ9EDp2Wefzfsf+z5QGoxlz21jPpaY/CrG+Zw2bVpj33uu4/XIEhAALXA8Y9DcGLszWsU0I8WgzzHIeXXxakae8iBAgAABAgQIECBAgAABAiNdIMZ0jG7u0VgmusFXKW6ERWvQSN/85jfTQw89VC1q+vPvf//79JGPfCRPehqNm6JO0QU/ulCvvvrqvbq5n3nmmbk3aVTi5z//eW6lGg2jqhSNraIVeLRg/djHPpbz/P73v18tzmOb/td//VeKbtnf+ta3ch7RuGrjjTfOvUvDYNNNN83D80UAduutt04rrrhinmBq/Pjx6bTTTmvkVb0YTpmxPxHw/NCHPpSDj3HzJnrKRpl90+2335623HLL7BENwWLSq3XWWScHLqt1h2JZbRMTUseQhHGMI2AcY7Vuu+22g5qkusrDc/cJCIAWOGYx+dFwx/voW82YNS7uTEkECBAgQIAAAQIECBAgQIDAvAWiu31MdBTPMQFSBAV7pgi2xfifMS9HNUFSz+XNer3hhhvmuUD++te/5mBezEb/y1/+Mt188805CHrqqaem6667Lhf3yU9+shEQjS76F198cTr00EPzsphgOQKYMUdIbHPDDTfkQGoMuxfrRYqWkdHCMYKON910Uw62RoA3xj6NYOvf/d3fpQg0RopA6RFHHJGiZWYESyNFY66eE0MNt8wwj3LGjh2bPv3pT+fAY+z/xIkTe7XCjBavEZj9xCc+kc4555w8gVSYLLHEEmmbbbZJF154Ya7fUCxjg9i3CHjG+J577rlnfh1B3uixG8FZaeQKCIAWOLarrLJKvtA0q6i4WEfT9viPLhEgQIAAAQIECBAgQIAAAQLzFogAVwQJV1555TzWZ39bxNigMTnT//3f/6VoXdiKFGP+xzijkaI1ZgQbJ0yYkDbaaKPc3T0+j9/8fVO0Uq1STDoZ84JEQLeaOyCGIdpll13yKlU3/k022SS3DI0PI+AaLUUjQBpd4GPegUmTJqU11lgjb3Pcccfl4OLyyy+fZ0E/5JBD8udHHXVUfm5GmRG8vOeee9Ktt96aJ86MjGMM2Ah0RrA20muvvZYDryussEJu8Zo/rP8TrXZPOeWUHMD+1Kc+lSeOHqplDAUQXhH8rVIVCO/PvFrHc/cLCIAWOIbRWvOHP/xh4w7FcIuMOzJxYVtqqaWGm5XtCRAgQIAAAQIECBAgQIDAiBeIMR+rgF4EOaMFYn8pGjDFGKGRIrhYjYvZ37rD+SzGP43Ud96KKhgZLRXnlq644oo8i/1JJ52UPvCBDzQe0VW8bw/UCCRGimH5egZRq/yruiy33HLVR/l5//33z8+33XZbfm5WmTvvvHNudRqtMKNVaqToBh/jGUe64447cgB4rbXWekN9o+t+dIN/7LHH0jXXXJPXr+o/GMs4vtFL96tf/WreNsYXPeuss/LrGNNUGrkCZoEvcGzjzkL8B/+nf/qnPMhyNCGPcTWq/6SDqULcAbnyyivzkCo9ogAALoVJREFUWCQxSPDJJ588mM1G9TpxQYw7S3HHa6A/bqMayM53rEAMdB53pmM24nhIBLpJILpQxTkcrQkkAt0kEBM+3H333XlMtJhVWyLQLQIxfl10FY0f9dGCTCLQTQJTp07NE/tEV+f+AnPN3JcjjzwyB80i6Bbd3OeWovv7D37wg9xK8vTTT8+tE+e2fjOXVQ7R87NvqpbF5zFeZ6SYaDkCha1IETiN39JVMLZZZUbrzWhdG2OJxrGPCai+853v5Ja3sR8RAI0ULVH7SxEEjbrE+RPjnw6UKq++lhEojUmfjj/++NylPmI1kfquN1C+Pu9OAQHQQsctmmnHf+IY2yIeSy65ZL4rE3d34otKXFSimf2iiy6aokl2zMoWs6DNmDEj/8eOH5TVDGxf+tKXGmOAFKp+VxYTAdC4m/P0008LgHblERy9lY6uKXHeRlcQAdDRex50657HYPLxdyyGaTEbfLcexdFZ7+p7w1NPPZUEQEfnOdCtex2zQcf3hvihH78rujnNnDkzj4cYjT+qFOMWRorWezEBTM8UYwHG+IVVkKPnMq+7Q6CahCYC+VXrv1bU/MEHH8zBrsj74YcfHtSN2hgHNFL8/o4xIzuhUU3Pcz1iBpHuuuuulgVAI/+YpKjqIt6sMpdeeukULVVjLNLDDz88nXHGGbklaAREYzKkqkt//G3uL8XkTJHm97vmd7/73RQB8bimxE37+NsvjXwBAdBCxzhmbY9BdaP1ZwxQHF9Uorl21WR7MNWI4GiM8RGzxEkECBAgQIAAAQIECBAYSQLR6q6/GadjHyMQUo2Z2HOfL7rootz1t+dnXhPoKxBBz+ihEikmEx7KhMKd1KimZwB0zTXXzPsTY5VGj9O+6b777ssznEd38flNEbd4/vnn81ihkUezyozJnqIVZ/yf32233XKAOSZaisDkf//3f6f1118/VzlaeMYNkWgY0jNF0DdS367+PdcZ6HV044/Jl4499thBBcIHysfn3ScgAFrwmMWdjPgDHbO7/ehHP8qB0LiYzCvFOBzxHzSaifcdk2Ne21pOgAABAgQIECBAgACBbhCIVnbREqtnC9DokhpBkOhB99a3vrXXbkQL0JhvQSIwL4H3vOc9uctzBPSGmqIbdgwxMb+pGkO053k9t7z664ZdjW0ZraSrtN122+VepGeffXbuQh7vqxQ9ymLiou9973vVR/P1fMEFF+TtYtb2SM0qM+Y1qVp0x//rKVOmpGjVef311+dy1l577fx/O95H/OT9739//jz+CZ9rr702vf3tb09xXOeW+rOsyu25LALkkapjNbc857as2n6gYz2v5XPL27LhCwiADt9wyDnETGvxiBnU4j96jFMZdzRjXI24oEWgtBr7Ly4GMSZGtP6UhiYQs+lFE/1ll112aBtam0CbBWKCs2g1HrNTSgS6TSC6SEULi/ntktRt+6u+I0cgvjfEjem4/koEukkgAoNx3g4nQNMp+xuzQ8dDGj0C8b0hfrNVXZ5buec9Z/1uZTk9837yySd7tTztuSy65Ueqhrqrlk2fPj2/jLGpq1TV/bzzzkt77rlnDhTuuOOOeab26Mq9ww475HFKN99889yVPLqXX3zxxbn7euQRQ+tFqsbWzG/6+ef73/9+YwKo2CYmCoqJkKqx3WNM0M997nO5+/hwyox9jJ6xMXlTlaKrfRXEjZau0Ro8bnDEhFRbbbVVHqsz1o0xjyNu8tOf/rRx3gzFcoMNNshFnnjiiSleR1D80ksvzXO0xFjgN954Y46/ROvSBx54IK9b+eU3c/mnCqRGfKdvy9W5nQtzydKiZgrUo94SgWICBx54YIzkXKsPNlysTAURIECAAAECBAgQIECAwMgQqAfJ8m/K+uTAHbtD9aBarR48rNUDh7mu8Ru43jCnVg8e1uqtGmu//e1vG8vqwb5avUVkrd5qs1afjbxWv5mRt6nf2KjV5xLJ+1hvPFWrN4zKn9cbS9TqAc78eb1FYa0+RmmtHkBulFPvpp7zr3BOOOGEWr1FZV4eZdWH5avVJ1ytFufn+tjtefnnP//5Wn1Sodree+9di3wi776pGWXWA5q1emOvWr1laa0+2VSt3oW/FrGCetCwV3H1gG2tHqSs1Sc2rtVnu6/FvoTD7373u8Z6Q7Ws3+ys7bTTTrV6wLVWb3BSqwdYa/XxXmv77LNP/mzy5Mm1euO02jHHHFOrB+izS721ea0+VmmtHpRulNvzRT2AWttvv/1q9YZrjeMQ+UQe8zoXeuYz0Ot60DvnG2VI8y8wJjZtZkBVXs0RiEGgn3jiiTwQ9Ejq9h53i7797W/nAajrF7jmYMmFAAECBAgQIECAAAECBEaFQD2wlGIcx3oAtNFicDTseIRu7r///jwh0YIL9u7MG/GDaN25zDLL5OU9xwodjM073/nOdOutt+YhKGLb6J0aXcz7jr3ZM6/hlBnbRqvfiHnEsBcTJ06ca6/XaOE5bdq0FC1hYxzSoe5fz3pXr6PlbUzG1DOvaI3cCZNdVXWsnvfaa6/04x//OE+GffLJJ1cfex6iQO//NUPc2OqtE7jllltSNF+PJt/XXXdd6wqSMwECBAgQIECAAAECBAgQINDRAhGoq7rC961oBBNjUqFmpAiixmNeaThlxraRYnzVeMwrxVAJ8Whm6m8fOzH42cx9Hu15vWm0A9h/AgQIECBAgAABAgQIECBAgMBoF9BBeLSfASN7/wVAR/bxtXcECBAgQIAAAQIECBAgQIAAgX4FYrKearKfnhMv9buyDwl0sYAAaBcfPFWfu0DcvaoPZjz3lSwl0KEC9YHOU32A8Q6tnWoRGFhgzpw5KcZ1kgh0m4DvDd12xNS3p4DvDT01vO4mAd8b2nu0pk6dmnbbbbc8FmZ0MT/ooIPSt771raQlaHuPi9JbI2AM0Na4yrUDBO666640ffr0tOWWW6aRNJFUB9CqQosFZs6cma666qo8zk0MSC4R6CaB+qyc6ZVXXkn1GURTfXbNbqq6uo5ygfjOEN8dYgz2FVZYYZRr2P1uEqjPaJyuvPLKVJ9RuWljAHbT/qtrdwtcffXVKSaeie8Nc5twp7v3snNrv95666Vzzz23cyuoZgSaKOCXSRMxZdVZAlXrz+q5s2qnNgQGFqjO2ep54DUtIdB5AnHeRgvQ6E4lEegmgeqaWz13U93VdXQLROvPSM7d0X0edOvez5o1K82ePTtFS1CJAAECrRQQAG2lrrwJECBAgAABAgQIECBAgAABAgQIEGirgC7wbeUfuPBx48albbbZJq277roDr2TJXAWqrpe6UsyVycIOFKjO3eq5A6uoSgQGFKjO2+p5wBUtINBhAtU5Wz13WPVUh8CAAtV3XefugEQWdLBAdd5Wzx1cVVUjQKDLBQRAO/QArrPOOnkMwA6tXldUa6211koRSB4/fnxX1FclCVQCb3nLW9IGG2yQ4lki0G0CkyZNyt3Yqh/k3VZ/9R29AmuuuWYaO3ZsWnnllUcvgj3vSoGll146bbjhhmmZZZbpyvqr9OgW2GSTTfLY4QsttNDohrD3BAi0XEAAtOXECmiXwGKLLZZWW221dhWvXALzLTBmzJg0ceLE+d7ehgTaKWDSuXbqK3s4AosuumhaffXVh5OFbQm0RSC+N0yYMKEtZSuUwHAF3PAfrqDtCRAYrIAxQAcrZT0CBAgQIECAAAECBAgQIECAAAECBLpOQAC06w6ZChMgQIAAAQIECBAgQIAAAQIECBAgMFgBAdDBSlmPAAECBAgQIECAAAECBAgQIECAAIGuExAA7bpDpsIECBAgQIAAAQIECBAgQIAAAQIECAxWQAB0sFLW6zqBWbNmpfvuuy+99tprXVd3FR7dArVaLd1///3p+eefH90Q9r4rBZ544on06KOPdmXdVXp0C7z00kvp3nvvTXPmzBndEPa+6wTie8MDDzyQZs6c2XV1V2ECTz75ZJoxYwYIAgQItFxAALTlxApol8D06dPTHXfckR577LF2VUG5BOZL4Kmnnkq33357mjZt2nxtbyMC7RS46aab0g033ODmUzsPgrLnS+Cee+5JU6dOFcCfLz0btVPgmWeeSbfddls+f9tZD2UTmB+Bm2++Od14441p9uzZ87O5bQgQIDBoAQHQQVNZsdsEXn/99Vzl6rnb6q++o1egOmer59ErYc+7UaA6b6vnbtwHdR6dAtU5Wz2PTgV73Y0C1TlbPXfjPqjz6BWoztvqefRK2HMCBFotsGCrC5A/gf4Ejj766HTqqaf2t6hpn7388sv5TuIiiyySFlpooablKyMCrRaI7pfRFXPBBRdMiy66aKuLkz+BpgrE0CNxDrvuNpVVZgUEohtxPMaMGZMfBYpUBIGmCETgKIZ8iu8Mq666alPylAmBUgIvvPBCvvaOHTs2velNg2uf9dBDD+Xq7bPPPim2kwiMdAHDSzXnCAuANsdRLoMUmDBhQl4zxoiLh0SAAAECI1Pg1VdfHZk7Zq8IECDQoQIx/v1dd93VobVTLQLNF3j44Yebn6kcCXSwwHrrrdfBtev8qo2p3+mudX411XAkCUTrIGO8jKQjal8IECDwN4FJkyalF198MZ177rlpscUW+9sCrwgQIECgJQK33nprOuSQQ9Kmm26azjrrrJaUIVMCnSQQPU0i+CmU0UlHRV1aLbDccsvl63yryxnJ+WsBOpKPbofu2+qrr96hNVMtAgQIEBiuwAILLJCzmDx5cho3btxws7M9AQIECMxDoBouZ/HFF09rr732PNa2mMDIEFh//fVHxo7YCwIEigkMbpCNYtVREAECBAgQIECAAAECBAgQIECAAAECBJonIADaPEs5ESBAgAABAgQIECBAgAABAgQIECDQYQICoB12QFSHAAECBAgQIECAAAECBAgQIECAAIHmCQiANs9STgQIECBAgAABAgQIECBAgAABAgQIdJiAAGiHHRDVIUCAAAECBAgQIECAAAECBAgQIECgeQICoM2zlBMBAgQIECBAgAABAgQIECBAgAABAh0mIADaYQdEdQgQIECAAAECBAgQIECAAAECBAgQaJ6AAGjzLOVEgAABAgQIECBAgAABAgQIECBAgECHCQiAdtgBUR0CBAgQIECAAAECBAgQIECAAAECBJonIADaPEs5ESBAgAABAgQIECBAgAABAgQIECDQYQICoB12QFSHAAECBAgQIECAAAECBAgQIECAAIHmCQiANs9STgQIECBAgAABAgQIECBAgAABAgQIdJiAAGiHHRDVIUCAAAECBAgQIECAAAECBAgQIECgeQICoM2zlBMBAgQIECBAgAABAgQIECBAgAABAh0mIADaYQdEdQgQIECAAAECBAgQIECAAAECBAgQaJ6AAGjzLOVEgAABAgQIECBAgAABAgQIECBAgECHCQiAdtgBUR0CBAgQINDNAuPGjUsLL7xwfnTzfqg7AQIEukUgrruRllxyyW6psnoSIECAAIHiAmNq9VS8VAUSIECAAAECI1LgzjvvTLNmzUqbbLLJiNw/O0WAAIFOFJgyZUpaf/310/jx4zuxeupEgAABAgTaLiAA2vZDoAIECBAgQIAAAQIECBAgQIAAAQIECLRKQBf4VsnKlwABAgQIECBAgAABAgQIECBAgACBtgsIgLb9EKgAAQIECBAgQIAAAQIECBAgQIAAAQKtEhAAbZWsfAkQIECAAAECBAgQIECAAAECBAgQaLuAAGjbD4EKECBAgAABAgQIECBAgAABAgQIECDQKgEB0FbJypcAAQIECBAgQIAAAQIECBAgQIAAgbYLCIC2/RCoAAECBAgQIECAAAECBAgQIECAAAECrRIQAG2VrHwJECBAgAABAgQIECBAgAABAgQIEGi7gABo2w+BChAgQIAAAQIECBAgQIAAAQIECBAg0CoBAdBWycqXAAECBAgQIECAAAECBAgQIECAAIG2CwiAtv0QqAABAgQIECBAgAABAgQIECBAgAABAq0SEABtlax8CRAgQIAAAQIECBAgQIAAAQIECBBou4AAaNsPgQoQIECAAAECBAgQIECAAAECBAgQINAqAQHQVsnKlwABAgQIECBAgAABAgQIECBAgACBtgsIgLb9EKgAAQIECBAgQIAAAQIECBAgQIAAAQKtEhAAbZWsfAkQIECAAAECBAgQIECAAAECBAgQaLuAAGjbD4EKECBAgAABAgQIECBAgAABAgQIECDQKgEB0FbJypcAAQIECBAgQIAAAQIECBAgQIAAgbYLCIC2/RCoAAECBAgQIECAAAECBAgQIECAAAECrRIQAG2VrHwJECBAgAABAgQIECBAgAABAgQIEGi7gABo2w+BChAgQIAAAQIECBAgQIAAAQIECBAg0CoBAdBWycqXAAECBAgQIECAAAECBAgQIECAAIG2CwiAtv0QqAABAgQIECBAgAABAgQIECBAgAABAq0SEABtlax8CRAgQIAAAQIECBAgQIAAAQIECBBou4AAaNsPgQoQIECAAAECBAgQIECAAAECBAgQINAqAQHQVsnKlwABAgQIECBAgAABAgQIECBAgACBtgsIgLb9EKgAAQIECBAgQIAAAQIECBAgQIAAAQKtEliwVRnLlwABAgQIEOgMgSeffDI9/vjjA1ZmzJgxackll0wrrrhiWmCBBQZc769//Wt65JFHBlw+2AVLLLFEetvb3vaG1V9//fX061//Ot1+++3p/vvvT2uttVaaNGlS2njjjdMiiyySXn311fT1r389HXrooW/Y1gcECBDoFIGpU6emWq02YHUWWmihfL0dN27cgOu0+nrbs+Co60033ZSvvfG3YtasWWnChAlpjTXWyI+VVlqpsfrZZ5+dNt9887Taaqs1PvOCAAECBAh0g8CY+h+8gf86d8MeqCMBAgQIECAwV4Frr702/ehHP0rnnXdeeuKJJwZcN4Kf48ePz4HHT37yk2mXXXZJCy74t3ulp512Wtp3330H3H6wCyZPnpx+9atf9Vo9Agb77LNPuv7661MEZN/1rnelOXPmpPg86rDDDjukv/zlL+m+++5LM2bM6LWtNwQIEOgkgSOOOCL94Q9/SFOmTJlrteJm0CqrrJK22267dOCBB6aJEyc21m/l9bYq5LnnnktHHXVU/vvw2GOP5Y8XXXTR9I53vCPFsnvuuSfFjamVV145ve9978vLzznnnHTxxRfnOlf5eCZAgAABAt0goAt8NxwldSRAgAABAsMQ2GKLLdJ3v/vddMstt6SFF164kdMee+yRonXon//85/yD9sgjj0yrrrpquuqqq9Luu++eVl999dwis9rg5Zdfzi/jR/tnPvOZdOGFF6Zrrrkm3XnnnfkRn1fpzDPPzJ9Fq6Jf/vKX6fjjj8+B1Vhe5VOtGwHNd7/73Tn4ecABB6QHH3wwXXfddenGG2/MLVcjMHDJJZek3//+9/n9a6+9Vm3qmQABAh0ncPjhh6crrrgiXyd7Vu6GG25I0bLzj3/8Yzr99NPTxz/+8fTAAw+kE088Ma255prpIx/5SJo9e3bepLpONvt6W9XnggsuyK07jz322BTBz/333z/deuutaebMmflafNddd6Xnn38+B3GjDnETLR7REj9aiEoECBAgQKDrBKIFqESAAAECBAiMDoF6y57o+ZEf9aDkG3a6/uO79ulPf7qxTv2Hb+3mm2/O6x1zzDG1euvMWv2H/Ru2iw+WWmqpxna/+c1v3rBOvUVRrd6SqFZv3dlr2U477ZS36/t5z5Uuv/zyWr3baF6vHjDtuchrAgQIdKRAvdV945pYH2ak3zrWg6L5ulhdl/faa69avdVlrVXX26jEKaecUnvTm96U61Yf+qR22WWX9Vu36sN6wLO23377NfblJz/5SbXIMwECBAgQ6BoBLUC7LmStwgQIECBAYP4Fll566bluHN3NTzrppLT99tvn9aIF0J577plfv/TSS7nb47bbbjvXPAZaGOPdHXzwwb1agL744ou5dWdss9FGGw20aS43WklFevTRRwdczwICBAh0isC8rrdRzxjnOIYnqVK0svzZz36WWnG9jTKuvvrq3NozurbHWKT1m1WN631Vh77P0S0+ehFUfxe0AO0r5D0BAgQIdIOAAGg3HCV1JECAAAECTRKot/pp5BRjbQ6UYky6Kk2bNi13lY8f5DvuuGP18Xw977zzzr0CoDHhUf22cc7ryiuvnGueMUbocsstZwzQuSpZSIBApwgM9nq76aabpnoL+ka1f/vb3+YAaLOvtzF8yKc+9anGNTdex2Rzg0nx9+J73/teHkYl/hZIBAgQIECg2wT+NrNBt9VcfQkQIECAAIGWCcS4oT3Tn/70p/SJT3wiveUtb+n58ZBfx+zv0cKpSj2DsDHhRozz+d73vrda3Os5WivFGHlagPZi8YYAgS4XiEBpTPxWH+oj70mM13zGGWc0/Xobrftvu+22XMYyyyyTYqzSoaSYsCnGLdUCdChq1iVAgACBThEQAO2UI6EeBAgQIECggwRiIqKeaY011ug1Q3HPZUN9HT/0q7TBBhukN7/5zXlijfjsQx/6UPr5z3+ettxyy2qVXs+HHnpoeuWVV3p95g0BAgS6XaDnNTeutzEpUjNSdb2NVpuHHXZYI8tdd901DaaLfmOD//+iPjZpbp3a93PvCRAgQIBApwv8rR9cp9dU/QgQIECAAIFiAjELe5Wi2/nEiROrt019jrHlvvjFLzbyfOqpp9JWW22VvvzlL/fqKl+tUJ9IJC2//PLVW88ECBDoeoFnn302TZ8+vbEfm222WeN1s17EUCYxw3uVVlttterlkJ5jLOcVVlhhSNtYmQABAgQIdIKAAGgnHAV1IECAAAECHSTw0EMPpfPPP79Row9/+MON1614ccghh6QddtihkXWMU3fkkUem+oz1qT7jfONzLwgQIDASBU444YTGuJwxFmjP62Gz9veOO+7oldWECRN6vfeGAAECBAiMdAFd4Ef6EbZ/BAgQIEBgCAJ33313mjx5cnr44YfzVrvssks67rjjhpDD0FeNLvAXX3xxbvUZ3SurSZHuvffeXJc99tgjz0zfc5KQoZdiCwIECHSWQMzEHq3djz766FyxsWPHpksvvbQlLe6nTp3aa+cH06r/xRdfTJdddlmv7fq++ed//ufUcyznvsu9J0CAAAECnSIgANopR0I9CBAgQIBAYYELL7wwPffccyl+hEfX85jo6Nprr83vY7KifffdN33+859PCyywQMtrFpOAHHXUUWn77bdP++23X7rzzjsbZf7v//5vilmRY1KQv//7v2987gUBAgS6RSAmDorhPhZffPF83Z0xY0aaMmVKeuKJJ9KCCy6YPvCBD+TlMSN8K1Lc3OqZVl111Z5v+30d1+XZs2enG264IZ1yyim9xv7cbrvtUowjKhEgQIAAgW4READtliOlngQIECBAoMkCjz32WA4szpkzJy2xxBK5y3l0vdx4443TP/zDP6T48Vs6xfifEYg99thj09e+9rXGhEePPPJIDo6eddZZaffddy9dLeURIEBgWAJxo+mmm27KAcXIaMUVV0wf+9jHUtxs+sd//Me08sorDyv/eW282GKL9VrlmWeemed4yjFGc1xv4xF/D6reADEOaExWF8slAgQIECDQLQICoN1ypNSTAAECBAg0WWD//fdPBxxwQJNzHX520SU+uoVG18q99947XX/99TnTCNTuueeeaaWVVkpbb7318AuSAwECBAoJRMvPyy+/vFBpbyxm/PjxvT6MSZfWXnvtXp/N7c26667bWBzd5wU/GxxeECBAgECXCJRv2tElMKpJgAABAgQItFdgnXXWSX/4wx/SZz/72UZFohXVF77whcZ7LwgQIEBg3gIbbbRRr5V6zjrfa8EAb6LVZ5UWWWSR6qVnAgQIECDQNQICoF1zqFSUAAECBAiMLIEYfzQm/JhbirHxTjzxxF6zIl933XXphRdemNtmlhEgQIBAD4H3v//9vcZz7jspUo9V+33Zc6Kjnq/7XdmHBAgQIECgAwUEQDvwoKgSAQIECBAYDQIR3IwZ3p9//vl57u4hhxzSa50YE1QiQIAAgcEJLLvssmmXXXZprPyTn/wk3XfffY33XhAgQIAAgZEuIAA60o+w/SNAgAABAh0qMHbs2DyO3EknnTTPGvYcqy7GCI0x6CQCBAgQGLxATC5Xjd35yiuvpIMOOmjwG1uTAAECBAh0uYAAaJcfQNUnQIAAAQJDEYgfvVV67bXXqpdNeZ6fvNdYY4301a9+Nd15551zrcODDz7YWL7hhhumhRdeuPHeCwIECHSiwPxcEwe7H/OT96qrrpp+8IMf5Bndo5yLLroo/exnPxtskdYjQIAAAQJdLSAA2tWHT+UJECBAgMDQBJ5++unGBk8++WTj9XBfzJo1K7300kuNbAab9+qrr55efvnlNHny5MZs741Merw47bTT8rsFFlggfeMb3+ixxEsCBAh0pkDP620M9dEzaDmcGs/v9TbK3H333VNcT6MlfaRdd901HXzwwWn27Nn5/UD/zJgxY6BFPidAgAABAl0hIADaFYdJJQkQIECAwPAFbr755nTPPfc0MrrkkktS/JBuRvrpT3/aK5vzzz8/xYzt80rRAjRSjOm51VZbpeOOOy498MADjc1efPHFPOt7/GCPiTeOOeaYtPXWWzeWe0GAAIFOFIgW9n1bV5577rlNqer8Xm+rwvfZZ580bdq09MEPfjDVarUUXeO32GKLdMYZZ6TbbrstzZkzJ68azzfeeGM69NBD02GHHZY/W2mlldKXvvSlKivPBAgQIECgawTG1P/o1bqmtipKgAABAgQIDFngF7/4RfrmN7+Zrr322je08ll66aXT5ptvnj772c/2mml9sIV89KMfTbfeemt+9N1m5ZVXTptsskmKCYw222yzvovz+3POOSd98YtfTF/+8pfTLbfckqKu0d19ueWWS+PHj0/Tp0/PraZi+5gN/l3vele/+fiQAAECnSKw22675evtQw899IYqbbTRRmmDDTbIwcY3LJzHB8O93vaX/VVXXZWOOOKIfP2dOXNmXmWRRRZJMWlStOR/9dVX0worrJDe97735UmUdt555xQT2EkECBAgQKDbBARAu+2IqS8BAgQIEBhBAo8//nh6+OGH06RJkxp79eyzz+ZWSPfff3+aMGFCigmQVlxxxcZyLwgQIECg+QKPPvpouuuuu/KNpxhnOQKfMeHcOuus0/zC5EiAAAECBAoLCIAWBlccAQIECBAgQIAAAQIECBAgQIAAAQLlBIwBWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgICoOWslUSAAAECBAgQIECAAAECBAgQIECAQGEBAdDC4IojQIAAAQIECBAgQIAAAQIECBAgQKCcgABoOWslESBAgAABAgQIECBAgAABAgQIECBQWEAAtDC44ggQIECAAAECBAgQIECAAAECBAgQKCcgAFrOWkkECBAgQIAAAQIECBAgQIAAAQIECBQWEAAtDK44AgQIECBAgAABAgQIECBAgAABAgTKCQiAlrNWEgECBAgQIECAAAECBAgQIECAAAEChQUEQAuDK44AAQIECBAgQIAAAQIECBAgQIAAgXICAqDlrJVEgAABAgQIECBAgAABAgQIECBAgEBhAQHQwuCKI0CAAAECBAgQIECAAAECBAgQIECgnIAAaDlrJREgQIAAAQIECBAgQIAAAQIECBAgUFhAALQwuOIIECBAgAABAgQIECBAgAABAgQIECgnIABazlpJBAgQIECAAAECBAgQIECAAAECBAgUFhAALQyuOAIECBAgQIAAAQIECBAgQIAAAQIEygkIgJazVhIBAgQIECBAgAABAgQIECBAgAABAoUFBEALgyuOAAECBAgQIECAAAECBAgQIECAAIFyAgKg5ayVRIAAAQIECBAgQIAAAQIECBAgQIBAYQEB0MLgiiNAgAABAgQIECBAgAABAgQIECBAoJyAAGg5ayURIECAAAECBAgQIECAAAECBAgQIFBYQAC0MLjiCBAgQIAAAQIECBAgQIAAAQIECBAoJyAAWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgICoOWslUSAAAECBAgQIECAAAECBAgQIECAQGEBAdDC4IojQIAAAQIECBAgQIAAAQIECBAgQKCcgABoOWslESBAgAABAgQIECBAgAABAgQIECBQWEAAtDC44ggQIECAAAECBAgQIECAAAECBAgQKCcgAFrOWkkECBAgQIAAAQIECBAgQIAAAQIECBQWEAAtDK44AgQIECBAgAABAgQIECBAgAABAgTKCQiAlrNWEgECBAgQIECAAAECBAgQIECAAAEChQUEQAuDK44AAQIECBAgQIAAAQIECBAgQIAAgXICAqDlrJVEgAABAgQIECBAgAABAgQIECBAgEBhAQHQwuCKI0CAAAECBAgQIECAAAECBAgQIECgnIAAaDlrJREgQIAAAQIECBAgQIAAAQIECBAgUFhAALQwuOIIECBAgAABAgQIECBAgAABAgQIECgnIABazlpJBAgQIECAAAECBAgQIECAAAECBAgUFhAALQyuOAIECBAgQIAAAQIECBAgQIAAAQIEygkIgJazVhIBAgQIECBAgAABAgQIECBAgAABAoUFBEALgyuOAAECBAgQIECAAAECBAgQIECAAIFyAgKg5ayVRIAAAQIECBAgQIAAAQIECBAgQIBAYQEB0MLgiiNAgAABAgQIECBAgAABAgQIECBAoJyAAGg5ayURIECAAAECBAgQIECAAAECBAgQIFBYQAC0MLjiCBAgQIAAAQIECBAgQIAAAQIECBAoJyAAWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgICoOWslUSAAAECBAgQIECAAAECBAgQIECAQGEBAdDC4IojQIAAAQIECBAgQIAAAQIECBAgQKCcgABoOWslESBAgAABAgQIECBAgAABAgQIECBQWEAAtDC44ggQIECAAAECBAgQIECAAAECBAgQKCcgAFrOWkkECBAgQIAAAQIECBAgQIAAAQIECBQWEAAtDK44AgQIECBAgAABAgQIECBAgAABAgTKCQiAlrNWEgECBAgQIECAAAECBAgQIECAAAEChQUEQAuDK44AAQIECBAgQIAAAQIECBAgQIAAgXICAqDlrJVEgAABAgQIECBAgAABAgQIECBAgEBhAQHQwuCKI0CAAAECBAgQIECAAAECBAgQIECgnIAAaDlrJREgQIAAAQIECBAgQIAAAQIECBAgUFhAALQwuOIIECBAgAABAgQIECBAgAABAgQIECgnIABazlpJBAgQIECAAAECBAgQIECAAAECBAgUFhAALQyuOAIECBAgQIAAAQIECBAgQIAAAQIEygkIgJazVhIBAgQIECBAgAABAgQIECBAgAABAoUFBEALgyuOAAECBAgQIECAAAECBAgQIECAAIFyAgKg5ayVRIAAAQIECBAgQIAAAQIECBAgQIBAYQEB0MLgiiNAgAABAgQIECBAgAABAgQIECBAoJyAAGg5ayURIECAAAECBAgQIECAAAECBAgQIFBYQAC0MLjiCBAgQIAAAQIECBAgQIAAAQIECBAoJyAAWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgICoOWslUSAAAECBAgQIECAAAECBAgQIECAQGEBAdDC4IojQIAAAQIECBAgQIAAAQIECBAgQKCcgABoOWslESBAgAABAgQIECBAgAABAgQIECBQWEAAtDC44ggQIECAAAECBAgQIECAAAECBAgQKCcgAFrOWkkECBAgQIAAAQIECBAgQIAAAQIECBQWEAAtDK44AgQIECBAgAABAgQIECBAgAABAgTKCQiAlrNWEgECBAgQIECAAAECBAgQIECAAAEChQUEQAuDK44AAQIECBAgQIAAAQIECBAgQIAAgXICAqDlrJVEgAABAgQIECBAgAABAgQIECBAgEBhAQHQwuCKI0CAAAECBAgQIECAAAECBAgQIECgnIAAaDlrJREgQIAAAQIECBAgQIAAAQIECBAgUFhAALQwuOIIECBAgAABAgQIECBAgAABAgQIECgnIABazlpJBAgQIECAAAECBAgQIECAAAECBAgUFhAALQyuOAIECBAgQIAAAQIECBAgQIAAAQIEygkIgJazVhIBAgQIECBAgAABAgQIECBAgAABAoUFBEALgyuOAAECBAgQIECAAAECBAgQIECAAIFyAgKg5ayVRIAAAQIECBAgQIAAAQIECBAgQIBAYQEB0MLgiiNAgAABAgQIECBAgAABAgQIECBAoJyAAGg5ayURIECAAAECBAgQIECAAAECBAgQIFBYQAC0MLjiCBAgQIAAAQIECBAgQIAAAQIECBAoJyAAWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgICoOWslUSAAAECBAgQIECAAAECBAgQIECAQGEBAdDC4IojQIAAAQIECBAgQIAAAQIECBAgQKCcgABoOWslESBAgAABAgQIECBAgAABAgQIECBQWEAAtDC44ggQIECAAAECBAgQIECAAAECBAgQKCcgAFrOWkkECBAgQIAAAQIECBAgQIAAAQIECBQWEAAtDK44AgQIECBAgAABAgQIECBAgAABAgTKCQiAlrNWEgECBAgQIECAAAECBAgQIECAAAEChQUEQAuDK44AAQIECBAgQIAAAQIECBAgQIAAgXICAqDlrJVEgAABAgQIECBAgAABAgQIECBAgEBhAQHQwuCKI0CAAAECBAgQIECAAAECBAgQIECgnIAAaDlrJREgQIAAAQIECBAgQIAAAQIECBAgUFhAALQwuOIIECBAgAABAgQIECBAgAABAgQIECgnIABazlpJBAgQIECAAAECBAgQIECAAAECBAgUFhAALQyuOAIECBAgQIAAAQIECBAgQIAAAQIEygkIgJazVhIBAgQIECBAgAABAgQIECBAgAABAoUFBEALgyuOAAECBAgQIECAAAECBAgQIECAAIFyAgKg5ayVRIAAAQIECBAgQIAAAQIECBAgQIBAYQEB0MLgiiNAgAABAgQIECBAgAABAgQIECBAoJyAAGg5ayURIECAAAECBAgQIECAAAECBAgQIFBYQAC0MLjiCBAgQIAAAQIECBAgQIAAAQIECBAoJyAAWs5aSQQIECBAgAABAgQIECBAgAABAgQIFBYQAC0MrjgCBAgQIECAAAECBAgQIECAAAECBMoJCICWs1YSAQIECBAgQIAAAQIECBAgQIAAAQKFBQRAC4MrjgABAgQIECBAgAABAgQIECBAgACBcgL/D7Gwt0L2FK9FAAAAAElFTkSuQmCC" width="672" /></p>
<pre class="r"><code>coefplot(m_time3,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>coefplot(m_time4,main = &#39;&#39;,
                   col = 1,
                   drop = c(&#39;Black&#39;, &#39;Female&#39;, &#39;Class&#39;, &#39;Republican&#39;, &#39;PolInterest&#39;),
                   value.lab = &#39;Estimate with __ci__ Confidence Interval&#39;)
legend(&#39;bottomright&#39;, xpd = T,
         col = 1, pch = c(20, 17, 15, 21, 24),
       cex = 0.7,
       legend = c(&#39;Federal&#39;, &#39;State&#39;, &#39;Local&#39;, 
                              &#39;Interpersonal I&#39;, &#39;Interpersonal II&#39;),
       title = &#39;Trust DV&#39;)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#### Resource Deprivation 

m_resource &lt;- feols(c(trstgov, trststate, trstloc, trstgen, trstgen2) ~ 
                  PTS*lower + PTG +
                  Time + Black + Female + Republican + PolInterest | study,
                weights = ~weight,
                data = datp)</code></pre>
<pre><code>## NOTE: 1,293 observations removed because of NA values (RHS: 1,293).
##       |-&gt; this msg only concerns the variables common to all estimations</code></pre>
<pre class="r"><code>summary(m_resource)</code></pre>
<pre><code>## Standard-errors: Clustered (study) 
## Dep. var.: trstgov
##              Estimate Std. Error   t value Pr(&gt;|t|)    
## PTS          0.139481   0.044684  3.121469 0.052412 .  
## lower        0.007161   0.016215  0.441645 0.688641    
## PTG          0.042700   0.031990  1.334775 0.274207    
## Time        -0.056563   0.015730 -3.595923 0.036868 *  
## Black        0.061265   0.014945  4.099256 0.026263 *  
## Female      -0.035450   0.009190 -3.857450 0.030786 *  
## Republican  -0.253469   0.045088 -5.621609 0.011130 *  
## PolInterest  0.007918   0.025631  0.308920 0.777593    
## PTS:lower   -0.134965   0.055028 -2.452651 0.091463 .  
## ---
## Dep. var.: trststate
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS          0.046748   0.055599  0.840797 0.4621925    
## lower       -0.003900   0.017819 -0.218874 0.8407935    
## PTG          0.053111   0.008048  6.598891 0.0070839 ** 
## Time        -0.048323   0.018520 -2.609219 0.0797387 .  
## Black        0.022438   0.013392  1.675425 0.1924443    
## Female      -0.027931   0.022577 -1.237126 0.3040501    
## Republican  -0.087987   0.028957 -3.038540 0.0559318 .  
## PolInterest  0.016509   0.035862  0.460354 0.6765790    
## PTS:lower   -0.131417   0.046267 -2.840402 0.0656249 .  
## ---
## Dep. var.: trstloc
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.022716   0.042317 -0.536803 0.6286601    
## lower       -0.021401   0.011905 -1.797591 0.1700897    
## PTG          0.044280   0.009939  4.455199 0.0210484 *  
## Time        -0.030207   0.017630 -1.713369 0.1851607    
## Black       -0.001210   0.020129 -0.060094 0.9558604    
## Female      -0.031916   0.015589 -2.047286 0.1331107    
## Republican  -0.084067   0.033330 -2.522291 0.0860036 .  
## PolInterest  0.083449   0.027071  3.082668 0.0540229 .  
## PTS:lower   -0.095131   0.015561 -6.113608 0.0087953 ** 
## ---
## Dep. var.: trstgen
##              Estimate Std. Error   t value  Pr(&gt;|t|)    
## PTS         -0.131469   0.013904 -9.455333 0.0025074 ** 
## lower       -0.067538   0.040010 -1.688037 0.1899878    
## PTG          0.034588   0.036809  0.939653 0.4167157    
## Time        -0.116957   0.013085 -8.938569 0.0029542 ** 
## Black       -0.108390   0.040630 -2.667732 0.0758368 .  
## Female      -0.070724   0.035398 -1.997935 0.1396051    
## Republican  -0.104080   0.029737 -3.500038 0.0394799 *  
## PolInterest  0.119237   0.032258  3.696350 0.0343648 *  
## PTS:lower   -0.076468   0.075279 -1.015790 0.3845243    
## ---
## Dep. var.: trstgen2
##              Estimate Std. Error    t value  Pr(&gt;|t|)    
## PTS         -0.198475   0.022802  -8.704420 0.0031915 ** 
## lower       -0.052597   0.026306  -1.999432 0.1394027    
## PTG          0.099523   0.030055   3.311306 0.0453478 *  
## Time        -0.044315   0.021570  -2.054477 0.1321945    
## Black       -0.080608   0.006463 -12.472678 0.0011108 ** 
## Female      -0.009367   0.017734  -0.528206 0.6339358    
## Republican  -0.071398   0.026069  -2.738853 0.0714071 .  
## PolInterest  0.093161   0.026584   3.504424 0.0393555 *  
## PTS:lower   -0.063194   0.048866  -1.293202 0.2865199</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Federal&#39;  = m_resource[1],
                           &#39;State&#39;  = m_resource[2],
                           &#39;Local&#39;  = m_resource[3],
                           &#39;Interpersonal I&#39;  = m_resource[4],
                           &#39;Interpersonal II&#39;  = m_resource[5]),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># mediation analysis
library(mediation)</code></pre>
<pre><code>## Loading required package: MASS</code></pre>
<pre><code>## 
## Attaching package: &#39;MASS&#39;</code></pre>
<pre><code>## The following object is masked from &#39;package:dplyr&#39;:
## 
##     select</code></pre>
<pre><code>## Loading required package: Matrix</code></pre>
<pre><code>## 
## Attaching package: &#39;Matrix&#39;</code></pre>
<pre><code>## The following objects are masked from &#39;package:OpenMx&#39;:
## 
##     %&amp;%, expm</code></pre>
<pre><code>## The following objects are masked from &#39;package:tidyr&#39;:
## 
##     expand, pack, unpack</code></pre>
<pre><code>## Loading required package: mvtnorm</code></pre>
<pre><code>## Loading required package: sandwich</code></pre>
<pre><code>## mediation: Causal Mediation Analysis
## Version: 4.5.0</code></pre>
<pre><code>## 
## Attaching package: &#39;mediation&#39;</code></pre>
<pre><code>## The following object is masked from &#39;package:psych&#39;:
## 
##     mediate</code></pre>
<pre class="r"><code>fit.med &lt;- lm(trstgov ~ PTS,
              data = datp)
fit.dv &lt;- lm(presvote20 ~ PTS + trstgov,
              data = datp)

med1 &lt;- mediate(fit.med, fit.dv,
                treat = &#39;PTS&#39;,
                mediator = &#39;trstgov&#39;,
                boot = T)</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>fit.med2 &lt;- lm(trstgen2 ~ PTS,
              data = datp)
fit.dv2 &lt;- lm(presvote20 ~ PTS + trstgen2,
              data = datp)

med2 &lt;- mediate(fit.med2, fit.dv2,
                treat = &#39;PTS&#39;,
                mediator = &#39;trstgen2&#39;,
                boot = T)</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>fit.med3 &lt;- lm(trstgen2 ~ PTG,
              data = datp)
fit.dv3 &lt;- lm(presvote20 ~ PTG + trstgen2,
              data = datp)

med3 &lt;- mediate(fit.med3, fit.dv3,
                treat = &#39;PTG&#39;,
                mediator = &#39;trstgen2&#39;,
                boot = T)</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>fit.med4 &lt;- lm(trstgen2 ~ PTG,
              data = datp)
fit.dv4 &lt;- lm(presvote20 ~ PTG + trstgen2,
              data = datp)

med4 &lt;- mediate(fit.med4, fit.dv4,
                treat = &#39;PTG&#39;,
                mediator = &#39;trstgen2&#39;,
                boot = T)</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>summary(med1); summary(med2); summary(med3); summary(med4)</code></pre>
<pre><code>## 
## Causal Mediation Analysis 
## 
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
## 
##                Estimate 95% CI Lower 95% CI Upper p-value    
## ACME           -0.00371     -0.00856         0.00   0.086 .  
## ADE            -0.18260     -0.23363        -0.13  &lt;2e-16 ***
## Total Effect   -0.18631     -0.23712        -0.14  &lt;2e-16 ***
## Prop. Mediated  0.01991     -0.00266         0.05   0.086 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Sample Size Used: 2945 
## 
## 
## Simulations: 1000</code></pre>
<pre><code>## 
## Causal Mediation Analysis 
## 
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
## 
##                Estimate 95% CI Lower 95% CI Upper p-value    
## ACME            -0.0233      -0.0337        -0.01  &lt;2e-16 ***
## ADE             -0.1598      -0.2114        -0.11  &lt;2e-16 ***
## Total Effect    -0.1831      -0.2340        -0.14  &lt;2e-16 ***
## Prop. Mediated   0.1271       0.0729         0.20  &lt;2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Sample Size Used: 2983 
## 
## 
## Simulations: 1000</code></pre>
<pre><code>## 
## Causal Mediation Analysis 
## 
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
## 
##                Estimate 95% CI Lower 95% CI Upper p-value    
## ACME           -0.00251     -0.00781         0.00    0.37    
## ADE            -0.08028     -0.12529        -0.03  &lt;2e-16 ***
## Total Effect   -0.08278     -0.12907        -0.04  &lt;2e-16 ***
## Prop. Mediated  0.03029     -0.03847         0.13    0.37    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Sample Size Used: 2984 
## 
## 
## Simulations: 1000</code></pre>
<pre><code>## 
## Causal Mediation Analysis 
## 
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
## 
##                Estimate 95% CI Lower 95% CI Upper p-value    
## ACME           -0.00251     -0.00785         0.00    0.36    
## ADE            -0.08028     -0.12572        -0.04  &lt;2e-16 ***
## Total Effect   -0.08278     -0.12891        -0.04  &lt;2e-16 ***
## Prop. Mediated  0.03029     -0.04034         0.11    0.36    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## Sample Size Used: 2984 
## 
## 
## Simulations: 1000</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;PTS Federal&#39;  = med1,
                           &#39;PTG Interpersonal II&#39; = med2,
                           &#39;PTS Federal&#39;  = med3,
                           &#39;PTG Interpersonal II&#39; = med4),
             output = &#39;latex&#39;,
             # stars = c(&#39;*&#39; = .05),
             # gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre><code>## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class &quot;mediate&quot;. The package tried a sequence of 2 helper functions:
## 
## performance::model_performance(model)
## broom::glance(model)
## 
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
## 
## https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html</code></pre>
<pre><code>## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class &quot;mediate&quot;. The package tried a sequence of 2 helper functions:
## 
## performance::model_performance(model)
## broom::glance(model)
## 
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
## 
## https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html

## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class &quot;mediate&quot;. The package tried a sequence of 2 helper functions:
## 
## performance::model_performance(model)
## broom::glance(model)
## 
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
## 
## https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html

## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class &quot;mediate&quot;. The package tried a sequence of 2 helper functions:
## 
## performance::model_performance(model)
## broom::glance(model)
## 
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
## 
## https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html</code></pre>
<pre class="r"><code>modelsummary(med1)</code></pre>
<pre><code>## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class &quot;mediate&quot;. The package tried a sequence of 2 helper functions:
## 
## performance::model_performance(model)
## broom::glance(model)
## 
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
## 
## https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html</code></pre>
<table class="table" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:center;">
 (1)
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
acme_0
</td>
<td style="text-align:center;">
−0.004
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(0.002)
</td>
</tr>
<tr>
<td style="text-align:left;">
acme_1
</td>
<td style="text-align:center;">
−0.004
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(0.002)
</td>
</tr>
<tr>
<td style="text-align:left;">
ade_0
</td>
<td style="text-align:center;">
−0.183
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(0.026)
</td>
</tr>
<tr>
<td style="text-align:left;">
ade_1
</td>
<td style="text-align:center;">
−0.183
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(0.026)
</td>
</tr>
</tbody>
</table>
<pre class="r"><code>plot(med1)</code></pre>
<p><img 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" width="672" /></p>
</div>
<div id="sem" class="section level3">
<h3>SEM</h3>
<pre class="r"><code>sem1 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi1 ~ black + hisp + female + lower + gop + newsint
  ptsi1 ~ black + hisp + female + lower + gop + newsint
  trstgov ~ ptgi1 + ptsi1 + black + hisp + female + lower + gop + newsint
  trststate ~ ptgi1 + ptsi1 + black + hisp + female + lower + gop + newsint
  trstloc ~ ptgi1 + ptsi1 + black + hisp + female + lower + gop + newsint
  trstgen ~ ptgi1 + ptsi1 + black + hisp + female + lower + gop + newsint
  trstgen2 ~ ptgi1 + ptsi1 + black + hisp + female + lower + gop + newsint
# residual correlations
  black ~~ female
  black ~~ lower
  female ~~ lower
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

fit1 &lt;- sem(sem1, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, cluster = &#39;study&#39;,
            data = datp)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 116 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female lower]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
##     The variance-covariance matrix of the estimated parameters (vcov)
##     does not appear to be positive definite! The smallest eigenvalue
##     (= -2.267549e-16) is smaller than zero. This may be a symptom that
##     the model is not identified.</code></pre>
<pre class="r"><code>summary(fit1)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 166 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       112
## 
##                                                   Used       Total
##   Number of observations                          3884        4000
##   Number of clusters [study]                         4            
##   Number of missing patterns                        39            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                              1779.948    1079.016
##   Degrees of freedom                               131         131
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.650
##     Yuan-Bentler correction (Mplus variant)                       
## 
## Parameter Estimates:
## 
##   Standard errors                        Robust.cluster
##   Information                                  Observed
##   Observed information based on                 Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.203    0.030   40.005    0.000
##     ptgi_3            1.223    0.041   29.923    0.000
##     ptgi_4            1.203    0.024   50.298    0.000
##     ptgi_5            1.258    0.052   24.434    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.047    0.035   29.684    0.000
##     ptsi_3            1.124    0.043   26.222    0.000
##     ptsi_4            1.218    0.019   64.629    0.000
##     ptsi_5            0.977    0.037   26.719    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.168    0.014   12.284    0.000
##     hisp              0.120    0.012    9.669    0.000
##     female            0.080    0.007   10.738    0.000
##     lower             0.013    0.007    1.911    0.056
##     gop               0.045    0.013    3.406    0.001
##     newsint          -0.008    0.024   -0.340    0.734
##   ptsi1 ~                                             
##     black             0.067    0.017    3.976    0.000
##     hisp              0.049    0.008    6.007    0.000
##     female            0.064    0.011    5.766    0.000
##     lower             0.045    0.014    3.273    0.001
##     gop              -0.069    0.008   -8.599    0.000
##     newsint          -0.059    0.025   -2.323    0.020
##   trstgov ~                                           
##     ptgi1             0.021    0.044    0.483    0.629
##     ptsi1             0.131    0.055    2.399    0.016
##     black             0.100    0.023    4.334    0.000
##     hisp              0.078    0.045    1.740    0.082
##     female           -0.033    0.015   -2.192    0.028
##     lower            -0.027    0.007   -4.115    0.000
##     gop              -0.194    0.055   -3.518    0.000
##     newsint           0.014    0.017    0.831    0.406
##   trststate ~                                         
##     ptgi1             0.080    0.017    4.632    0.000
##     ptsi1             0.007    0.053    0.133    0.894
##     black             0.032    0.004    8.036    0.000
##     hisp             -0.006    0.028   -0.224    0.823
##     female           -0.041    0.026   -1.578    0.114
##     lower            -0.031    0.009   -3.472    0.001
##     gop              -0.093    0.034   -2.693    0.007
##     newsint           0.018    0.026    0.678    0.498
##   trstloc ~                                           
##     ptgi1             0.057    0.018    3.158    0.002
##     ptsi1            -0.054    0.042   -1.280    0.201
##     black             0.002    0.005    0.333    0.739
##     hisp              0.001    0.034    0.017    0.987
##     female           -0.038    0.012   -3.153    0.002
##     lower            -0.040    0.011   -3.823    0.000
##     gop              -0.088    0.028   -3.171    0.002
##     newsint           0.083    0.017    4.806    0.000
##   trstgen ~                                           
##     ptgi1             0.091    0.046    1.984    0.047
##     ptsi1            -0.176    0.056   -3.143    0.002
##     black            -0.120    0.022   -5.357    0.000
##     hisp             -0.132    0.057   -2.330    0.020
##     female           -0.065    0.023   -2.839    0.005
##     lower            -0.076    0.028   -2.667    0.008
##     gop              -0.098    0.031   -3.127    0.002
##     newsint           0.116    0.024    4.824    0.000
##   trstgen2 ~                                          
##     ptgi1             0.161    0.041    3.902    0.000
##     ptsi1            -0.263    0.024  -11.037    0.000
##     black            -0.104    0.019   -5.463    0.000
##     hisp             -0.094    0.044   -2.121    0.034
##     female           -0.017    0.010   -1.716    0.086
##     lower            -0.059    0.011   -5.317    0.000
##     gop              -0.075    0.023   -3.272    0.001
##     newsint           0.109    0.023    4.737    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.003    0.000    7.270    0.000
##     lower             0.023    0.002   10.744    0.000
##   female ~~                                           
##     lower             0.024    0.005    4.795    0.000
##  .ptgi1 ~~                                            
##    .ptsi1             0.012    0.001   10.340    0.000
##  .trstgov ~~                                          
##    .trststate         0.062    0.004   17.045    0.000
##    .trstgen           0.033    0.004    8.253    0.000
##  .trststate ~~                                        
##    .trstgen           0.033    0.004    7.691    0.000
##  .trstgov ~~                                          
##    .trstloc           0.057    0.004   13.145    0.000
##    .trstgen2          0.031    0.002   18.733    0.000
##  .trststate ~~                                        
##    .trstloc           0.071    0.005   14.422    0.000
##    .trstgen2          0.034    0.001   28.633    0.000
##  .trstloc ~~                                          
##    .trstgen           0.035    0.003   10.111    0.000
##    .trstgen2          0.040    0.002   24.110    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.076    0.003   25.567    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.519    0.029   17.673    0.000
##    .ptgi_2            0.376    0.039    9.653    0.000
##    .ptgi_3            0.390    0.033   11.889    0.000
##    .ptgi_4            0.477    0.030   15.645    0.000
##    .ptgi_5            0.310    0.037    8.481    0.000
##    .ptsi_1            0.285    0.018   16.107    0.000
##    .ptsi_2            0.257    0.019   13.310    0.000
##    .ptsi_3            0.341    0.025   13.834    0.000
##    .ptsi_4            0.287    0.018   15.742    0.000
##    .ptsi_5            0.349    0.017   19.958    0.000
##    .trstgov           0.435    0.047    9.194    0.000
##    .trststate         0.458    0.034   13.653    0.000
##    .trstloc           0.467    0.007   70.322    0.000
##    .trstgen           0.440    0.019   23.726    0.000
##    .trstgen2          0.559    0.045   12.290    0.000
##     black             0.122    0.002   63.006    0.000
##     female            0.515    0.003  188.879    0.000
##     lower             0.471    0.010   45.172    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.048    0.003   16.676    0.000
##    .ptgi_2            0.036    0.001   44.951    0.000
##    .ptgi_3            0.058    0.003   21.717    0.000
##    .ptgi_4            0.041    0.003   14.619    0.000
##    .ptgi_5            0.048    0.001   75.809    0.000
##    .ptsi_1            0.045    0.002   18.510    0.000
##    .ptsi_2            0.036    0.001   30.016    0.000
##    .ptsi_3            0.044    0.002   27.943    0.000
##    .ptsi_4            0.031    0.003   10.011    0.000
##    .ptsi_5            0.060    0.002   31.517    0.000
##    .trstgov           0.096    0.005   18.892    0.000
##    .trststate         0.101    0.004   27.048    0.000
##    .trstloc           0.099    0.004   25.382    0.000
##    .trstgen           0.228    0.007   32.203    0.000
##    .trstgen2          0.089    0.001   72.689    0.000
##     black             0.107    0.001   73.196    0.000
##     female            0.250    0.000 3014.299    0.000
##     lower             0.249    0.001  422.538    0.000
##    .ptgi1             0.044    0.003   14.905    0.000
##    .ptsi1             0.052    0.002   30.786    0.000</code></pre>
<pre class="r"><code>p_sem1 &lt;- graph_sem(model = fit1)

## analyzing by type of traumatic event

fit2 &lt;- sem(sem1, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, cluster = &#39;study&#39;,
            group = &#39;Type&#39;,
            data = datp)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable &#39;Type&#39; contains missing values</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 13 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 1 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 33 cases were deleted in group 4 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 7 cases were deleted in group 3 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 2 cases were deleted in group 2 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female lower]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
##     The variance-covariance matrix of the estimated parameters (vcov)
##     does not appear to be positive definite! The smallest eigenvalue
##     (= -2.502884e-14) is smaller than zero. This may be a symptom that
##     the model is not identified.</code></pre>
<pre class="r"><code>summary(fit2)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 489 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       560
## 
##   Number of observations per group:               Used       Total
##     1                                              912         925
##     0                                               78          79
##     4                                             1399        1432
##     3                                              446         453
##     2                                              131         133
##   Number of clusters [study]:                                     
##     1                                                4            
##     0                                                4            
##     4                                                4            
##     3                                                4            
##     2                                                4            
##   Number of missing patterns per group:                           
##     1                                               14            
##     0                                                4            
##     4                                               15            
##     3                                               10            
##     2                                                8            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                              2722.199    1905.878
##   Degrees of freedom                               655         655
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.428
##     Yuan-Bentler correction (Mplus variant)                       
##   Test statistic for each group:
##     1                                          764.881     535.512
##     0                                          353.492     247.488
##     4                                          814.351     570.147
##     3                                          360.689     252.527
##     2                                          428.786     300.204
## 
## Parameter Estimates:
## 
##   Standard errors                        Robust.cluster
##   Information                                  Observed
##   Observed information based on                 Hessian
## 
## 
## Group 1 [1]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.163    0.050   23.457    0.000
##     ptgi_3            1.203    0.080   15.092    0.000
##     ptgi_4            1.187    0.065   18.141    0.000
##     ptgi_5            1.207    0.066   18.230    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            0.979    0.011   90.918    0.000
##     ptsi_3            1.134    0.090   12.565    0.000
##     ptsi_4            1.161    0.044   26.348    0.000
##     ptsi_5            1.032    0.049   21.041    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.203    0.005   39.859    0.000
##     hisp              0.088    0.003   26.453    0.000
##     female            0.086    0.009    9.341    0.000
##     lower            -0.004    0.006   -0.601    0.548
##     gop               0.031    0.009    3.309    0.001
##     newsint          -0.022    0.024   -0.939    0.348
##   ptsi1 ~                                             
##     black             0.079    0.039    2.015    0.044
##     hisp             -0.001    0.021   -0.053    0.958
##     female            0.007    0.019    0.364    0.716
##     lower             0.039    0.031    1.244    0.214
##     gop              -0.051    0.015   -3.360    0.001
##     newsint          -0.084    0.053   -1.600    0.110
##   trstgov ~                                           
##     ptgi1            -0.079    0.098   -0.809    0.418
##     ptsi1             0.188    0.079    2.367    0.018
##     black             0.157    0.054    2.891    0.004
##     hisp              0.166    0.031    5.286    0.000
##     female           -0.022    0.012   -1.816    0.069
##     lower            -0.065    0.015   -4.327    0.000
##     gop              -0.214    0.061   -3.510    0.000
##     newsint           0.019    0.057    0.328    0.743
##   trststate ~                                         
##     ptgi1             0.009    0.092    0.103    0.918
##     ptsi1            -0.014    0.096   -0.146    0.884
##     black             0.060    0.028    2.163    0.031
##     hisp             -0.024    0.039   -0.622    0.534
##     female           -0.007    0.050   -0.136    0.892
##     lower            -0.057    0.029   -1.923    0.055
##     gop              -0.075    0.029   -2.589    0.010
##     newsint           0.046    0.060    0.771    0.441
##   trstloc ~                                           
##     ptgi1             0.041    0.120    0.339    0.735
##     ptsi1            -0.046    0.090   -0.510    0.610
##     black             0.007    0.036    0.188    0.851
##     hisp             -0.018    0.016   -1.132    0.257
##     female           -0.044    0.017   -2.603    0.009
##     lower            -0.088    0.036   -2.419    0.016
##     gop              -0.062    0.033   -1.856    0.064
##     newsint           0.070    0.064    1.099    0.272
##   trstgen ~                                           
##     ptgi1            -0.151    0.148   -1.017    0.309
##     ptsi1            -0.015    0.133   -0.114    0.909
##     black            -0.086    0.079   -1.079    0.281
##     hisp             -0.021    0.101   -0.205    0.837
##     female            0.006    0.085    0.073    0.942
##     lower            -0.089    0.076   -1.161    0.246
##     gop              -0.074    0.058   -1.292    0.196
##     newsint           0.044    0.084    0.520    0.603
##   trstgen2 ~                                          
##     ptgi1             0.182    0.078    2.326    0.020
##     ptsi1            -0.250    0.117   -2.139    0.032
##     black            -0.158    0.038   -4.158    0.000
##     hisp             -0.134    0.050   -2.675    0.007
##     female           -0.011    0.032   -0.333    0.739
##     lower            -0.060    0.038   -1.559    0.119
##     gop              -0.039    0.034   -1.154    0.249
##     newsint           0.025    0.071    0.349    0.727
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.003    0.004    0.743    0.458
##     lower             0.017    0.006    2.667    0.008
##   female ~~                                           
##     lower             0.021    0.006    3.458    0.001
##  .ptgi1 ~~                                            
##    .ptsi1             0.015    0.003    4.938    0.000
##  .trstgov ~~                                          
##    .trststate         0.051    0.006    9.309    0.000
##    .trstgen           0.031    0.004    8.696    0.000
##  .trststate ~~                                        
##    .trstgen           0.025    0.004    5.877    0.000
##  .trstgov ~~                                          
##    .trstloc           0.047    0.005    9.915    0.000
##    .trstgen2          0.026    0.003    7.980    0.000
##  .trststate ~~                                        
##    .trstloc           0.061    0.005   11.366    0.000
##    .trstgen2          0.025    0.007    3.640    0.000
##  .trstloc ~~                                          
##    .trstgen           0.033    0.004    8.208    0.000
##    .trstgen2          0.040    0.002   15.960    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.077    0.002   33.914    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.552    0.025   21.710    0.000
##    .ptgi_2            0.431    0.022   19.706    0.000
##    .ptgi_3            0.442    0.030   14.766    0.000
##    .ptgi_4            0.510    0.025   20.131    0.000
##    .ptgi_5            0.317    0.022   14.594    0.000
##    .ptsi_1            0.281    0.040    6.984    0.000
##    .ptsi_2            0.253    0.046    5.445    0.000
##    .ptsi_3            0.339    0.050    6.827    0.000
##    .ptsi_4            0.278    0.040    6.912    0.000
##    .ptsi_5            0.340    0.052    6.559    0.000
##    .trstgov           0.440    0.083    5.310    0.000
##    .trststate         0.427    0.085    5.010    0.000
##    .trstloc           0.503    0.053    9.460    0.000
##    .trstgen           0.500    0.115    4.355    0.000
##    .trstgen2          0.624    0.090    6.899    0.000
##     black             0.100    0.008   12.135    0.000
##     female            0.431    0.012   36.310    0.000
##     lower             0.408    0.012   33.023    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.042    0.004   10.553    0.000
##    .ptgi_2            0.031    0.003    9.238    0.000
##    .ptgi_3            0.048    0.007    7.194    0.000
##    .ptgi_4            0.034    0.004    9.735    0.000
##    .ptgi_5            0.046    0.004   11.735    0.000
##    .ptsi_1            0.041    0.007    5.580    0.000
##    .ptsi_2            0.032    0.004    7.963    0.000
##    .ptsi_3            0.041    0.003   13.559    0.000
##    .ptsi_4            0.030    0.003    8.695    0.000
##    .ptsi_5            0.054    0.005   10.662    0.000
##    .trstgov           0.084    0.005   15.404    0.000
##    .trststate         0.099    0.005   19.579    0.000
##    .trstloc           0.098    0.006   15.587    0.000
##    .trstgen           0.242    0.005   49.938    0.000
##    .trstgen2          0.090    0.003   33.752    0.000
##     black             0.090    0.007   13.652    0.000
##     female            0.245    0.002  150.518    0.000
##     lower             0.241    0.002  142.078    0.000
##    .ptgi1             0.043    0.005    9.155    0.000
##    .ptsi1             0.044    0.005    8.060    0.000
## 
## 
## Group 2 [0]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            0.947    0.131    7.206    0.000
##     ptgi_3            1.053    0.245    4.297    0.000
##     ptgi_4            1.447    0.276    5.233    0.000
##     ptgi_5            1.367    0.305    4.489    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            0.815    0.238    3.417    0.001
##     ptsi_3            1.456    0.449    3.245    0.001
##     ptsi_4            1.331    0.370    3.594    0.000
##     ptsi_5            0.946    0.209    4.533    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.202    0.041    4.880    0.000
##     hisp              0.075    0.069    1.078    0.281
##     female            0.097    0.008   12.331    0.000
##     lower             0.143    0.037    3.855    0.000
##     gop               0.072    0.033    2.187    0.029
##     newsint           0.071    0.153    0.461    0.645
##   ptsi1 ~                                             
##     black            -0.082    0.096   -0.862    0.389
##     hisp              0.057    0.117    0.492    0.623
##     female            0.023    0.028    0.832    0.406
##     lower             0.078    0.021    3.797    0.000
##     gop              -0.061    0.057   -1.072    0.284
##     newsint          -0.098    0.162   -0.604    0.546
##   trstgov ~                                           
##     ptgi1            -0.055    0.026   -2.119    0.034
##     ptsi1            -0.077    0.293   -0.262    0.793
##     black             0.126    0.080    1.569    0.117
##     hisp             -0.221    0.112   -1.967    0.049
##     female            0.083    0.017    4.800    0.000
##     lower            -0.039    0.114   -0.339    0.734
##     gop              -0.226    0.100   -2.269    0.023
##     newsint           0.026    0.112    0.231    0.817
##   trststate ~                                         
##     ptgi1            -0.392    0.178   -2.201    0.028
##     ptsi1             0.337    0.200    1.688    0.091
##     black             0.091    0.068    1.337    0.181
##     hisp             -0.349    0.088   -3.990    0.000
##     female            0.251    0.082    3.053    0.002
##     lower             0.019    0.082    0.231    0.818
##     gop              -0.069    0.075   -0.922    0.356
##     newsint           0.085    0.067    1.282    0.200
##   trstloc ~                                           
##     ptgi1            -0.308    0.159   -1.940    0.052
##     ptsi1            -0.048    0.317   -0.152    0.879
##     black             0.015    0.086    0.178    0.859
##     hisp             -0.357    0.047   -7.644    0.000
##     female            0.135    0.072    1.872    0.061
##     lower             0.021    0.102    0.202    0.840
##     gop              -0.065    0.069   -0.948    0.343
##     newsint           0.041    0.112    0.362    0.718
##   trstgen ~                                           
##     ptgi1             0.528    0.398    1.326    0.185
##     ptsi1            -0.554    0.387   -1.432    0.152
##     black             0.204    0.327    0.624    0.533
##     hisp             -0.302    0.125   -2.409    0.016
##     female            0.016    0.149    0.111    0.912
##     lower            -0.230    0.160   -1.437    0.151
##     gop              -0.091    0.012   -7.514    0.000
##     newsint          -0.015    0.171   -0.086    0.931
##   trstgen2 ~                                          
##     ptgi1             0.257    0.124    2.071    0.038
##     ptsi1            -0.183    0.101   -1.806    0.071
##     black             0.187    0.117    1.603    0.109
##     hisp             -0.216    0.170   -1.274    0.203
##     female            0.027    0.075    0.365    0.715
##     lower            -0.088    0.045   -1.963    0.050
##     gop              -0.016    0.049   -0.323    0.747
##     newsint          -0.011    0.226   -0.047    0.962
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.029    0.038    0.764    0.445
##     lower            -0.046    0.034   -1.341    0.180
##   female ~~                                           
##     lower            -0.008    0.029   -0.286    0.775
##  .ptgi1 ~~                                            
##    .ptsi1             0.009    0.005    1.676    0.094
##  .trstgov ~~                                          
##    .trststate         0.036    0.010    3.772    0.000
##    .trstgen           0.007    0.008    0.833    0.405
##  .trststate ~~                                        
##    .trstgen           0.010    0.006    1.591    0.112
##  .trstgov ~~                                          
##    .trstloc           0.031    0.006    5.056    0.000
##    .trstgen2          0.006    0.008    0.756    0.450
##  .trststate ~~                                        
##    .trstloc           0.032    0.010    3.291    0.001
##    .trstgen2          0.003    0.006    0.524    0.601
##  .trstloc ~~                                          
##    .trstgen           0.018    0.003    5.294    0.000
##    .trstgen2          0.007    0.002    4.203    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.046    0.006    8.008    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.374    0.120    3.126    0.002
##    .ptgi_2            0.281    0.135    2.081    0.037
##    .ptgi_3            0.194    0.114    1.708    0.088
##    .ptgi_4            0.202    0.187    1.081    0.280
##    .ptgi_5            0.028    0.144    0.191    0.849
##    .ptsi_1            0.175    0.175    0.999    0.318
##    .ptsi_2            0.114    0.126    0.906    0.365
##    .ptsi_3            0.245    0.194    1.267    0.205
##    .ptsi_4            0.195    0.196    0.994    0.320
##    .ptsi_5            0.276    0.176    1.568    0.117
##    .trstgov           0.431    0.104    4.140    0.000
##    .trststate         0.426    0.087    4.910    0.000
##    .trstloc           0.556    0.063    8.859    0.000
##    .trstgen           0.383    0.106    3.604    0.000
##    .trstgen2          0.607    0.241    2.515    0.012
##     black             0.113    0.110    1.032    0.302
##     female            0.566    0.042   13.354    0.000
##     lower             0.465    0.093    5.011    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.055    0.011    4.823    0.000
##    .ptgi_2            0.046    0.011    4.079    0.000
##    .ptgi_3            0.073    0.028    2.634    0.008
##    .ptgi_4            0.021    0.003    6.667    0.000
##    .ptgi_5            0.050    0.016    3.168    0.002
##    .ptsi_1            0.019    0.004    4.938    0.000
##    .ptsi_2            0.018    0.003    6.175    0.000
##    .ptsi_3            0.026    0.008    3.252    0.001
##    .ptsi_4            0.009    0.005    1.746    0.081
##    .ptsi_5            0.078    0.034    2.295    0.022
##    .trstgov           0.063    0.012    5.068    0.000
##    .trststate         0.057    0.011    5.001    0.000
##    .trstloc           0.063    0.007    9.190    0.000
##    .trstgen           0.168    0.005   37.163    0.000
##    .trstgen2          0.053    0.003   15.662    0.000
##     black             0.100    0.085    1.183    0.237
##     female            0.246    0.006   43.546    0.000
##     lower             0.261    0.033    7.799    0.000
##    .ptgi1             0.031    0.011    2.817    0.005
##    .ptsi1             0.023    0.011    2.092    0.036
## 
## 
## Group 3 [4]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.285    0.080   16.014    0.000
##     ptgi_3            1.265    0.086   14.761    0.000
##     ptgi_4            1.217    0.077   15.831    0.000
##     ptgi_5            1.333    0.121   11.003    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.083    0.049   22.265    0.000
##     ptsi_3            1.083    0.038   28.178    0.000
##     ptsi_4            1.236    0.010  119.217    0.000
##     ptsi_5            0.933    0.026   35.812    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.158    0.023    6.870    0.000
##     hisp              0.131    0.018    7.364    0.000
##     female            0.071    0.019    3.746    0.000
##     lower             0.010    0.019    0.541    0.589
##     gop               0.051    0.023    2.217    0.027
##     newsint          -0.002    0.032   -0.060    0.952
##   ptsi1 ~                                             
##     black             0.076    0.040    1.922    0.055
##     hisp              0.088    0.012    7.248    0.000
##     female            0.090    0.015    6.080    0.000
##     lower             0.054    0.002   21.597    0.000
##     gop              -0.058    0.008   -7.051    0.000
##     newsint          -0.014    0.022   -0.625    0.532
##   trstgov ~                                           
##     ptgi1             0.069    0.060    1.148    0.251
##     ptsi1             0.053    0.045    1.192    0.233
##     black             0.082    0.036    2.288    0.022
##     hisp              0.100    0.029    3.417    0.001
##     female           -0.028    0.021   -1.363    0.173
##     lower            -0.007    0.026   -0.284    0.776
##     gop              -0.179    0.037   -4.822    0.000
##     newsint           0.036    0.015    2.421    0.015
##   trststate ~                                         
##     ptgi1             0.155    0.035    4.441    0.000
##     ptsi1            -0.025    0.036   -0.696    0.486
##     black            -0.000    0.031   -0.012    0.991
##     hisp              0.017    0.044    0.390    0.697
##     female           -0.073    0.027   -2.695    0.007
##     lower            -0.021    0.020   -1.042    0.297
##     gop              -0.085    0.015   -5.844    0.000
##     newsint           0.020    0.027    0.738    0.461
##   trstloc ~                                           
##     ptgi1             0.122    0.043    2.813    0.005
##     ptsi1            -0.081    0.032   -2.540    0.011
##     black             0.005    0.009    0.582    0.560
##     hisp              0.019    0.042    0.442    0.659
##     female           -0.034    0.027   -1.283    0.200
##     lower            -0.010    0.012   -0.806    0.420
##     gop              -0.080    0.019   -4.214    0.000
##     newsint           0.111    0.015    7.448    0.000
##   trstgen ~                                           
##     ptgi1             0.162    0.088    1.838    0.066
##     ptsi1            -0.223    0.095   -2.348    0.019
##     black            -0.106    0.044   -2.421    0.015
##     hisp             -0.180    0.066   -2.714    0.007
##     female           -0.126    0.026   -4.788    0.000
##     lower            -0.075    0.029   -2.532    0.011
##     gop              -0.127    0.019   -6.680    0.000
##     newsint           0.172    0.058    2.975    0.003
##   trstgen2 ~                                          
##     ptgi1             0.139    0.079    1.764    0.078
##     ptsi1            -0.295    0.038   -7.750    0.000
##     black            -0.080    0.021   -3.755    0.000
##     hisp             -0.044    0.020   -2.151    0.032
##     female           -0.027    0.033   -0.819    0.413
##     lower            -0.039    0.009   -4.456    0.000
##     gop              -0.100    0.011   -9.411    0.000
##     newsint           0.139    0.046    3.036    0.002
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female           -0.002    0.006   -0.283    0.777
##     lower             0.021    0.007    2.982    0.003
##   female ~~                                           
##     lower             0.022    0.012    1.851    0.064
##  .ptgi1 ~~                                            
##    .ptsi1             0.011    0.001   14.441    0.000
##  .trstgov ~~                                          
##    .trststate         0.060    0.004   14.604    0.000
##    .trstgen           0.029    0.005    6.057    0.000
##  .trststate ~~                                        
##    .trstgen           0.034    0.008    4.344    0.000
##  .trstgov ~~                                          
##    .trstloc           0.056    0.007    8.502    0.000
##    .trstgen2          0.029    0.004    6.862    0.000
##  .trststate ~~                                        
##    .trstloc           0.068    0.006   10.732    0.000
##    .trstgen2          0.035    0.004    9.764    0.000
##  .trstloc ~~                                          
##    .trstgen           0.035    0.006    5.605    0.000
##    .trstgen2          0.036    0.003   11.401    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.070    0.003   25.531    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.511    0.032   15.919    0.000
##    .ptgi_2            0.346    0.047    7.419    0.000
##    .ptgi_3            0.366    0.043    8.618    0.000
##    .ptgi_4            0.476    0.039   12.218    0.000
##    .ptgi_5            0.321    0.050    6.430    0.000
##    .ptsi_1            0.268    0.023   11.678    0.000
##    .ptsi_2            0.249    0.024   10.435    0.000
##    .ptsi_3            0.339    0.024   14.025    0.000
##    .ptsi_4            0.280    0.032    8.889    0.000
##    .ptsi_5            0.358    0.024   14.872    0.000
##    .trstgov           0.389    0.050    7.781    0.000
##    .trststate         0.462    0.046    9.990    0.000
##    .trstloc           0.407    0.025   16.104    0.000
##    .trstgen           0.447    0.072    6.172    0.000
##    .trstgen2          0.532    0.073    7.328    0.000
##     black             0.128    0.015    8.305    0.000
##     female            0.609    0.023   26.970    0.000
##     lower             0.512    0.007   69.428    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.053    0.006    9.025    0.000
##    .ptgi_2            0.039    0.002   20.036    0.000
##    .ptgi_3            0.065    0.005   13.708    0.000
##    .ptgi_4            0.047    0.003   17.865    0.000
##    .ptgi_5            0.046    0.004   11.397    0.000
##    .ptsi_1            0.048    0.002   21.679    0.000
##    .ptsi_2            0.042    0.001   32.728    0.000
##    .ptsi_3            0.051    0.003   15.600    0.000
##    .ptsi_4            0.032    0.003   12.107    0.000
##    .ptsi_5            0.063    0.003   24.773    0.000
##    .trstgov           0.094    0.008   11.927    0.000
##    .trststate         0.098    0.007   14.679    0.000
##    .trstloc           0.091    0.007   13.524    0.000
##    .trstgen           0.217    0.005   41.090    0.000
##    .trstgen2          0.086    0.002   48.350    0.000
##     black             0.111    0.011    9.727    0.000
##     female            0.238    0.005   48.320    0.000
##     lower             0.250    0.000  741.801    0.000
##    .ptgi1             0.042    0.005    8.554    0.000
##    .ptsi1             0.058    0.003   17.539    0.000
## 
## 
## Group 4 [3]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.209    0.012  102.439    0.000
##     ptgi_3            1.270    0.112   11.369    0.000
##     ptgi_4            1.277    0.085   15.061    0.000
##     ptgi_5            1.312    0.056   23.473    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.073    0.098   10.972    0.000
##     ptsi_3            1.213    0.154    7.851    0.000
##     ptsi_4            1.249    0.122   10.280    0.000
##     ptsi_5            0.951    0.107    8.919    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.165    0.054    3.073    0.002
##     hisp              0.185    0.011   16.339    0.000
##     female            0.090    0.023    3.942    0.000
##     lower             0.021    0.015    1.428    0.153
##     gop               0.053    0.021    2.539    0.011
##     newsint          -0.005    0.025   -0.218    0.828
##   ptsi1 ~                                             
##     black             0.072    0.021    3.439    0.001
##     hisp              0.149    0.032    4.593    0.000
##     female            0.015    0.007    1.980    0.048
##     lower            -0.002    0.018   -0.097    0.923
##     gop              -0.053    0.018   -3.028    0.002
##     newsint          -0.072    0.022   -3.266    0.001
##   trstgov ~                                           
##     ptgi1            -0.006    0.112   -0.056    0.956
##     ptsi1             0.321    0.121    2.654    0.008
##     black             0.150    0.030    5.021    0.000
##     hisp             -0.045    0.067   -0.663    0.508
##     female            0.007    0.020    0.370    0.712
##     lower            -0.007    0.031   -0.234    0.815
##     gop              -0.201    0.097   -2.074    0.038
##     newsint           0.030    0.061    0.495    0.621
##   trststate ~                                         
##     ptgi1             0.039    0.051    0.753    0.452
##     ptsi1             0.126    0.151    0.832    0.405
##     black             0.062    0.032    1.955    0.051
##     hisp             -0.136    0.104   -1.305    0.192
##     female            0.017    0.025    0.696    0.486
##     lower            -0.018    0.048   -0.367    0.713
##     gop              -0.146    0.067   -2.165    0.030
##     newsint           0.045    0.086    0.524    0.601
##   trstloc ~                                           
##     ptgi1            -0.076    0.110   -0.689    0.491
##     ptsi1            -0.009    0.092   -0.095    0.924
##     black             0.077    0.012    6.214    0.000
##     hisp             -0.026    0.094   -0.280    0.780
##     female            0.005    0.033    0.156    0.876
##     lower            -0.055    0.052   -1.063    0.288
##     gop              -0.159    0.058   -2.743    0.006
##     newsint           0.036    0.045    0.806    0.420
##   trstgen ~                                           
##     ptgi1             0.179    0.069    2.595    0.009
##     ptsi1            -0.117    0.198   -0.593    0.553
##     black            -0.156    0.119   -1.308    0.191
##     hisp             -0.226    0.150   -1.510    0.131
##     female            0.024    0.046    0.519    0.604
##     lower            -0.115    0.051   -2.266    0.023
##     gop              -0.024    0.035   -0.697    0.486
##     newsint           0.134    0.081    1.647    0.099
##   trstgen2 ~                                          
##     ptgi1             0.146    0.098    1.489    0.136
##     ptsi1            -0.257    0.066   -3.928    0.000
##     black            -0.022    0.031   -0.706    0.480
##     hisp             -0.175    0.101   -1.724    0.085
##     female            0.056    0.031    1.785    0.074
##     lower            -0.116    0.051   -2.270    0.023
##     gop              -0.068    0.030   -2.242    0.025
##     newsint           0.075    0.071    1.056    0.291
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female           -0.008    0.009   -0.882    0.378
##     lower             0.022    0.007    3.110    0.002
##   female ~~                                           
##     lower             0.006    0.003    1.801    0.072
##  .ptgi1 ~~                                            
##    .ptsi1             0.011    0.003    4.215    0.000
##  .trstgov ~~                                          
##    .trststate         0.061    0.004   13.663    0.000
##    .trstgen           0.029    0.005    6.245    0.000
##  .trststate ~~                                        
##    .trstgen           0.031    0.006    5.546    0.000
##  .trstgov ~~                                          
##    .trstloc           0.057    0.004   15.816    0.000
##    .trstgen2          0.034    0.003   13.608    0.000
##  .trststate ~~                                        
##    .trstloc           0.073    0.005   15.672    0.000
##    .trstgen2          0.031    0.006    5.125    0.000
##  .trstloc ~~                                          
##    .trstgen           0.039    0.006    6.342    0.000
##    .trstgen2          0.037    0.002   16.808    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.073    0.010    7.107    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.530    0.032   16.353    0.000
##    .ptgi_2            0.372    0.033   11.334    0.000
##    .ptgi_3            0.392    0.032   12.433    0.000
##    .ptgi_4            0.452    0.035   12.868    0.000
##    .ptgi_5            0.297    0.029   10.309    0.000
##    .ptsi_1            0.278    0.038    7.250    0.000
##    .ptsi_2            0.239    0.023   10.374    0.000
##    .ptsi_3            0.318    0.037    8.520    0.000
##    .ptsi_4            0.266    0.035    7.574    0.000
##    .ptsi_5            0.317    0.019   16.787    0.000
##    .trstgov           0.401    0.079    5.067    0.000
##    .trststate         0.411    0.071    5.796    0.000
##    .trstloc           0.507    0.027   18.478    0.000
##    .trstgen           0.311    0.075    4.122    0.000
##    .trstgen2          0.563    0.090    6.228    0.000
##     black             0.114    0.008   13.722    0.000
##     female            0.462    0.022   20.528    0.000
##     lower             0.418    0.017   24.046    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.043    0.005    8.172    0.000
##    .ptgi_2            0.028    0.001   23.060    0.000
##    .ptgi_3            0.047    0.007    6.875    0.000
##    .ptgi_4            0.035    0.008    4.582    0.000
##    .ptgi_5            0.045    0.003   14.185    0.000
##    .ptsi_1            0.043    0.008    5.361    0.000
##    .ptsi_2            0.028    0.004    6.756    0.000
##    .ptsi_3            0.033    0.006    5.379    0.000
##    .ptsi_4            0.027    0.007    3.989    0.000
##    .ptsi_5            0.063    0.004   17.278    0.000
##    .trstgov           0.087    0.008   10.833    0.000
##    .trststate         0.103    0.003   30.809    0.000
##    .trstloc           0.107    0.006   18.608    0.000
##    .trstgen           0.215    0.012   17.725    0.000
##    .trstgen2          0.089    0.004   21.764    0.000
##     black             0.101    0.006   15.744    0.000
##     female            0.249    0.002  144.678    0.000
##     lower             0.243    0.004   66.316    0.000
##    .ptgi1             0.044    0.005    8.177    0.000
##    .ptsi1             0.035    0.006    5.744    0.000
## 
## 
## Group 5 [2]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            0.738    0.200    3.699    0.000
##     ptgi_3            1.011    0.058   17.416    0.000
##     ptgi_4            0.864    0.046   18.753    0.000
##     ptgi_5            0.659    0.105    6.299    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            0.918    0.077   11.928    0.000
##     ptsi_3            1.009    0.105    9.634    0.000
##     ptsi_4            0.964    0.185    5.206    0.000
##     ptsi_5            0.813    0.073   11.126    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 ~                                             
##     black             0.097    0.072    1.340    0.180
##     hisp              0.001    0.147    0.009    0.993
##     female            0.193    0.068    2.830    0.005
##     lower             0.043    0.092    0.472    0.637
##     gop               0.054    0.104    0.521    0.602
##     newsint          -0.160    0.057   -2.792    0.005
##   ptsi1 ~                                             
##     black             0.002    0.064    0.038    0.970
##     hisp              0.170    0.088    1.935    0.053
##     female            0.050    0.030    1.660    0.097
##     lower            -0.024    0.077   -0.309    0.757
##     gop              -0.159    0.019   -8.352    0.000
##     newsint          -0.057    0.067   -0.858    0.391
##   trstgov ~                                           
##     ptgi1             0.135    0.145    0.934    0.350
##     ptsi1             0.516    0.163    3.158    0.002
##     black             0.064    0.081    0.790    0.430
##     hisp              0.073    0.055    1.329    0.184
##     female           -0.128    0.026   -4.918    0.000
##     lower            -0.155    0.082   -1.886    0.059
##     gop              -0.198    0.047   -4.234    0.000
##     newsint          -0.239    0.154   -1.555    0.120
##   trststate ~                                         
##     ptgi1             0.105    0.112    0.940    0.347
##     ptsi1             0.425    0.105    4.048    0.000
##     black             0.023    0.071    0.331    0.740
##     hisp              0.011    0.118    0.092    0.926
##     female           -0.073    0.016   -4.547    0.000
##     lower            -0.164    0.084   -1.949    0.051
##     gop               0.009    0.050    0.183    0.855
##     newsint          -0.107    0.138   -0.772    0.440
##   trstloc ~                                           
##     ptgi1             0.069    0.105    0.661    0.509
##     ptsi1             0.189    0.080    2.376    0.018
##     black            -0.047    0.079   -0.593    0.553
##     hisp              0.136    0.037    3.654    0.000
##     female           -0.016    0.044   -0.357    0.721
##     lower            -0.118    0.037   -3.189    0.001
##     gop              -0.046    0.075   -0.608    0.543
##     newsint           0.035    0.092    0.378    0.706
##   trstgen ~                                           
##     ptgi1             0.545    0.390    1.398    0.162
##     ptsi1            -0.160    0.505   -0.317    0.752
##     black            -0.165    0.165   -0.998    0.318
##     hisp              0.087    0.083    1.058    0.290
##     female           -0.361    0.137   -2.643    0.008
##     lower            -0.022    0.021   -1.045    0.296
##     gop              -0.031    0.112   -0.281    0.779
##     newsint           0.064    0.199    0.323    0.746
##   trstgen2 ~                                          
##     ptgi1             0.348    0.101    3.454    0.001
##     ptsi1             0.122    0.192    0.633    0.526
##     black            -0.031    0.102   -0.308    0.758
##     hisp             -0.085    0.095   -0.892    0.372
##     female           -0.054    0.055   -0.987    0.324
##     lower            -0.115    0.061   -1.866    0.062
##     gop               0.047    0.056    0.843    0.399
##     newsint           0.186    0.074    2.502    0.012
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.025    0.021    1.177    0.239
##     lower             0.031    0.004    8.154    0.000
##   female ~~                                           
##     lower             0.029    0.018    1.594    0.111
##  .ptgi1 ~~                                            
##    .ptsi1             0.005    0.005    1.119    0.263
##  .trstgov ~~                                          
##    .trststate         0.058    0.006    9.016    0.000
##    .trstgen           0.040    0.032    1.268    0.205
##  .trststate ~~                                        
##    .trstgen           0.037    0.021    1.774    0.076
##  .trstgov ~~                                          
##    .trstloc           0.052    0.014    3.790    0.000
##    .trstgen2          0.031    0.011    2.947    0.003
##  .trststate ~~                                        
##    .trstloc           0.052    0.011    4.713    0.000
##    .trstgen2          0.029    0.007    4.485    0.000
##  .trstloc ~~                                          
##    .trstgen           0.043    0.015    2.776    0.005
##    .trstgen2          0.041    0.009    4.394    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.068    0.009    7.233    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.494    0.031   15.825    0.000
##    .ptgi_2            0.447    0.075    5.958    0.000
##    .ptgi_3            0.442    0.013   35.351    0.000
##    .ptgi_4            0.533    0.042   12.708    0.000
##    .ptgi_5            0.379    0.049    7.714    0.000
##    .ptsi_1            0.465    0.053    8.801    0.000
##    .ptsi_2            0.339    0.068    4.982    0.000
##    .ptsi_3            0.440    0.084    5.235    0.000
##    .ptsi_4            0.387    0.097    3.975    0.000
##    .ptsi_5            0.424    0.068    6.234    0.000
##    .trstgov           0.831    0.107    7.764    0.000
##    .trststate         0.658    0.116    5.652    0.000
##    .trstloc           0.584    0.121    4.805    0.000
##    .trstgen           0.527    0.139    3.790    0.000
##    .trstgen2          0.525    0.060    8.829    0.000
##     black             0.120    0.028    4.376    0.000
##     female            0.454    0.047    9.565    0.000
##     lower             0.436    0.013   34.732    0.000
##    .ptgi1             0.000                           
##    .ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.047    0.013    3.532    0.000
##    .ptgi_2            0.064    0.017    3.671    0.000
##    .ptgi_3            0.060    0.009    6.959    0.000
##    .ptgi_4            0.053    0.014    3.723    0.000
##    .ptgi_5            0.071    0.004   16.721    0.000
##    .ptsi_1            0.057    0.013    4.435    0.000
##    .ptsi_2            0.034    0.006    5.863    0.000
##    .ptsi_3            0.035    0.003   11.964    0.000
##    .ptsi_4            0.051    0.016    3.167    0.002
##    .ptsi_5            0.052    0.007    7.210    0.000
##    .trstgov           0.105    0.014    7.563    0.000
##    .trststate         0.082    0.008   10.300    0.000
##    .trstloc           0.082    0.012    6.787    0.000
##    .trstgen           0.204    0.028    7.150    0.000
##    .trstgen2          0.073    0.004   16.262    0.000
##     black             0.106    0.021    5.070    0.000
##     female            0.248    0.004   57.102    0.000
##     lower             0.244    0.001  382.224    0.000
##    .ptgi1             0.052    0.016    3.192    0.001
##    .ptsi1             0.037    0.007    5.353    0.000</code></pre>
</div>
</div>
</div>
<div id="analysis" class="section level1">
<h1>Analysis</h1>
<div id="sem-approach" class="section level2">
<h2>SEM Approach</h2>
<pre class="r"><code>sem1 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi ~ black + female + income
  ptsi ~ black + female + income
  trstgov ~ ptgi + ptsi + black + female + income + gop
  trststate ~ ptgi + ptsi + black + female + income + gop
  trstgen ~ ptgi + ptsi + black + female + income + gop
# residual correlations
  black ~~ female
  black ~~ income
  female ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

fit1 &lt;- sem(sem1, missing = &#39;fiml&#39;, data = dat1)</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>summary(fit1)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 134 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        77
## 
##   Number of observations                          1555
##   Number of missing patterns                         8
## 
## Model Test User Model:
##                                                        
##   Test statistic                              36662.912
##   Degrees of freedom                                130
##   P-value (Chi-square)                            0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.110    0.033   33.332    0.000
##     ptgi_3            1.021    0.037   27.682    0.000
##     ptgi_4            1.140    0.035   32.694    0.000
##     ptgi_5            1.065    0.035   30.475    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            0.883    0.027   33.160    0.000
##     ptsi_3            0.991    0.029   33.783    0.000
##     ptsi_4            1.030    0.025   41.581    0.000
##     ptsi_5            0.895    0.031   29.039    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi ~                                              
##     black             0.168    0.023    7.371    0.000
##     female            0.070    0.016    4.452    0.000
##     income            0.010    0.029    0.330    0.742
##   ptsi ~                                              
##     black             0.002    0.022    0.096    0.923
##     female            0.099    0.015    6.454    0.000
##     income           -0.126    0.029   -4.402    0.000
##   trstgov ~                                           
##     ptgi              0.087    0.022    3.868    0.000
##     ptsi             -0.116    0.022   -5.164    0.000
##     black            -0.008    0.016   -0.534    0.593
##     female            0.008    0.011    0.771    0.441
##     income            0.007    0.019    0.380    0.704
##     gop              -0.139    0.014   -9.671    0.000
##   trststate ~                                         
##     ptgi              0.103    0.026    3.957    0.000
##     ptsi             -0.159    0.026   -6.113    0.000
##     black            -0.024    0.019   -1.276    0.202
##     female           -0.007    0.013   -0.538    0.591
##     income            0.021    0.023    0.902    0.367
##     gop              -0.099    0.017   -5.817    0.000
##   trstgen ~                                           
##     ptgi              0.011    0.052    0.219    0.826
##     ptsi             -0.298    0.052   -5.697    0.000
##     black            -0.202    0.038   -5.293    0.000
##     female            0.014    0.025    0.536    0.592
##     income            0.071    0.047    1.517    0.129
##     gop              -0.122    0.034   -3.597    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.004    0.004    1.059    0.289
##     income           -0.011    0.002   -4.744    0.000
##   female ~~                                           
##     income           -0.007    0.004   -1.971    0.049
##   ptgi1 ~~                                            
##     ptsi1             0.027    0.002   11.161    0.000
##  .trstgov ~~                                          
##    .trststate         0.026    0.001   18.448    0.000
##    .trstgen           0.021    0.003    8.274    0.000
##  .trststate ~~                                        
##    .trstgen           0.025    0.003    8.273    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.574    0.009   62.508    0.000
##    .ptgi_2            0.388    0.009   42.109    0.000
##    .ptgi_3            0.365    0.010   36.826    0.000
##    .ptgi_4            0.509    0.010   52.399    0.000
##    .ptgi_5            0.353    0.009   37.490    0.000
##    .ptsi_1            0.276    0.009   30.918    0.000
##    .ptsi_2            0.264    0.009   29.813    0.000
##    .ptsi_3            0.411    0.010   42.270    0.000
##    .ptsi_4            0.297    0.009   32.043    0.000
##    .ptsi_5            0.413    0.010   42.289    0.000
##    .ptgi              0.375    0.018   20.553    0.000
##    .ptsi              0.334    0.018   18.718    0.000
##    .trstgov           0.395    0.016   25.328    0.000
##    .trststate         0.426    0.018   23.163    0.000
##    .trstgen           0.557    0.037   14.993    0.000
##     black             0.129    0.008   15.150    0.000
##     female            0.490    0.013   38.655    0.000
##     income            0.457    0.007   65.013    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.042    0.002   21.124    0.000
##    .ptgi_2            0.027    0.002   17.478    0.000
##    .ptgi_3            0.057    0.003   22.180    0.000
##    .ptgi_4            0.035    0.002   18.801    0.000
##    .ptgi_5            0.038    0.002   20.168    0.000
##    .ptsi_1            0.024    0.001   17.034    0.000
##    .ptsi_2            0.040    0.002   21.233    0.000
##    .ptsi_3            0.044    0.002   20.500    0.000
##    .ptsi_4            0.027    0.002   17.406    0.000
##    .ptsi_5            0.059    0.003   22.424    0.000
##    .ptgi              0.078    0.003   25.169    0.000
##    .ptsi              0.075    0.003   25.111    0.000
##    .trstgov           0.040    0.001   27.161    0.000
##    .trststate         0.056    0.002   27.151    0.000
##    .trstgen           0.233    0.008   27.701    0.000
##     black             0.112    0.004   27.884    0.000
##     female            0.250    0.009   27.884    0.000
##     income            0.074    0.003   27.475    0.000
##     ptgi1             0.065    0.004   16.121    0.000
##     ptsi1             0.077    0.004   19.079    0.000</code></pre>
<pre class="r"><code>sem2 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi ~ black + female + income
  ptsi ~ black + female + income
  trstgov ~ ptgi + ptsi + black + female + income + gop
  trststate ~ ptgi + ptsi + black + female + income + gop
  trstloc ~ ptgi + ptsi + black + female + income + gop
  trstgen ~ ptgi + ptsi + black + female + income + gop
  trstgen2 ~ ptgi + ptsi + black + female + income + gop
# residual correlations
  black ~~ female
  black ~~ income
  female ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

fit2 &lt;- sem(sem2, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat2)</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.

## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre><code>## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
##     The variance-covariance matrix of the estimated parameters (vcov)
##     does not appear to be positive definite! The smallest eigenvalue
##     (= 5.023472e-20) is close to zero. This may be a symptom that the
##     model is not identified.</code></pre>
<pre class="r"><code>summary(fit2)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 170 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       100
## 
##   Number of observations                          1000
##   Number of missing patterns                        25
##   Sampling weights variable                     weight
## 
## Model Test User Model:
##                                                Standard      Scaled
##   Test Statistic                              18532.762   19091.261
##   Degrees of freedom                                150         150
##   P-value (Chi-square)                            0.000       0.000
##   Scaling correction factor                                   0.971
##     Yuan-Bentler correction (Mplus variant)                        
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.182    0.050   23.610    0.000
##     ptgi_3            1.222    0.069   17.731    0.000
##     ptgi_4            1.198    0.065   18.389    0.000
##     ptgi_5            1.207    0.070   17.327    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.136    0.093   12.266    0.000
##     ptsi_3            1.242    0.063   19.569    0.000
##     ptsi_4            1.275    0.055   23.168    0.000
##     ptsi_5            1.028    0.062   16.579    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi ~                                              
##     black             0.179    0.031    5.760    0.000
##     female            0.114    0.024    4.848    0.000
##     income           -0.085    0.044   -1.941    0.052
##   ptsi ~                                              
##     black             0.136    0.040    3.364    0.001
##     female            0.121    0.022    5.418    0.000
##     income           -0.011    0.039   -0.275    0.784
##   trstgov ~                                           
##     ptgi              0.037    0.052    0.706    0.480
##     ptsi              0.028    0.053    0.525    0.600
##     black             0.062    0.048    1.282    0.200
##     female           -0.032    0.025   -1.276    0.202
##     income            0.055    0.043    1.272    0.204
##     gop              -0.154    0.029   -5.325    0.000
##   trststate ~                                         
##     ptgi              0.057    0.057    0.998    0.318
##     ptsi             -0.080    0.057   -1.398    0.162
##     black             0.035    0.045    0.788    0.431
##     female           -0.011    0.026   -0.432    0.666
##     income            0.050    0.046    1.100    0.271
##     gop              -0.033    0.030   -1.091    0.275
##   trstloc ~                                           
##     ptgi              0.050    0.060    0.826    0.409
##     ptsi             -0.060    0.060   -1.001    0.317
##     black             0.002    0.046    0.055    0.956
##     female           -0.016    0.027   -0.603    0.546
##     income            0.119    0.046    2.559    0.011
##     gop              -0.034    0.031   -1.085    0.278
##   trstgen ~                                           
##     ptgi              0.005    0.069    0.072    0.942
##     ptsi             -0.101    0.069   -1.460    0.144
##     black            -0.070    0.049   -1.419    0.156
##     female           -0.096    0.035   -2.700    0.007
##     income            0.378    0.062    6.054    0.000
##     gop              -0.010    0.039   -0.260    0.795
##   trstgen2 ~                                          
##     ptgi              0.044    0.050    0.893    0.372
##     ptsi             -0.247    0.048   -5.201    0.000
##     black            -0.064    0.036   -1.759    0.079
##     female           -0.029    0.023   -1.230    0.219
##     income            0.231    0.045    5.129    0.000
##     gop              -0.027    0.028   -0.993    0.321
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.003    0.006    0.422    0.673
##     income           -0.013    0.003   -3.867    0.000
##   female ~~                                           
##     income           -0.019    0.006   -3.360    0.001
##   ptgi1 ~~                                            
##     ptsi1             0.018    0.003    6.576    0.000
##  .trstgov ~~                                          
##    .trststate         0.065    0.004   16.214    0.000
##    .trstgen           0.021    0.005    4.150    0.000
##  .trststate ~~                                        
##    .trstgen           0.029    0.005    5.651    0.000
##  .trstgov ~~                                          
##    .trstloc           0.064    0.004   15.423    0.000
##    .trstgen2          0.028    0.003    7.950    0.000
##  .trststate ~~                                        
##    .trstloc           0.077    0.004   18.263    0.000
##    .trstgen2          0.034    0.004    9.227    0.000
##  .trstloc ~~                                          
##    .trstgen           0.034    0.005    6.218    0.000
##    .trstgen2          0.040    0.004   10.827    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.068    0.005   14.786    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.607    0.014   44.143    0.000
##    .ptgi_2            0.498    0.014   35.993    0.000
##    .ptgi_3            0.508    0.016   32.196    0.000
##    .ptgi_4            0.586    0.015   39.845    0.000
##    .ptgi_5            0.432    0.016   27.573    0.000
##    .ptsi_1            0.306    0.014   22.040    0.000
##    .ptsi_2            0.276    0.014   19.785    0.000
##    .ptsi_3            0.357    0.015   23.799    0.000
##    .ptsi_4            0.302    0.015   20.361    0.000
##    .ptsi_5            0.360    0.015   23.560    0.000
##    .ptgi              0.484    0.029   16.772    0.000
##    .ptsi              0.246    0.024   10.069    0.000
##    .trstgov           0.363    0.038    9.509    0.000
##    .trststate         0.389    0.040    9.833    0.000
##    .trstloc           0.424    0.042    9.990    0.000
##    .trstgen           0.296    0.051    5.792    0.000
##    .trstgen2          0.536    0.035   15.480    0.000
##     black             0.121    0.013    9.589    0.000
##     female            0.513    0.019   27.049    0.000
##     income            0.400    0.011   34.838    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.045    0.004   11.479    0.000
##    .ptgi_2            0.033    0.003   10.674    0.000
##    .ptgi_3            0.052    0.006    9.279    0.000
##    .ptgi_4            0.038    0.004    9.954    0.000
##    .ptgi_5            0.049    0.005   10.837    0.000
##    .ptsi_1            0.046    0.004   11.181    0.000
##    .ptsi_2            0.038    0.006    5.938    0.000
##    .ptsi_3            0.039    0.004    9.183    0.000
##    .ptsi_4            0.029    0.003    9.449    0.000
##    .ptsi_5            0.063    0.006   10.660    0.000
##    .ptgi              0.075    0.004   21.427    0.000
##    .ptsi              0.071    0.004   20.033    0.000
##    .trstgov           0.093    0.004   21.313    0.000
##    .trststate         0.102    0.004   24.938    0.000
##    .trstloc           0.107    0.004   24.835    0.000
##    .trstgen           0.211    0.006   32.939    0.000
##    .trstgen2          0.088    0.004   24.708    0.000
##     black             0.107    0.010   11.123    0.000
##     female            0.250    0.001  492.705    0.000
##     income            0.084    0.004   20.783    0.000
##     ptgi1             0.055    0.005   10.337    0.000
##     ptsi1             0.053    0.005   10.181    0.000</code></pre>
<pre class="r"><code>sem3 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi ~ black + female + income
  ptsi ~ black + female + income
  trstgov ~ ptgi + ptsi + black + female + income + gop
  trststate ~ ptgi + ptsi + black + female + income + gop
  trstloc ~ ptgi + ptsi + black + female + income + gop
  trstgen ~ ptgi + ptsi + black + female + income + gop
  trstgen2 ~ ptgi + ptsi + black + female + income + gop
# residual correlations
  black ~~ female
  black ~~ income
  female ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

fit3 &lt;- sem(sem3, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat3)</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre><code>## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
##     The variance-covariance matrix of the estimated parameters (vcov)
##     does not appear to be positive definite! The smallest eigenvalue
##     (= -4.242597e-20) is smaller than zero. This may be a symptom that
##     the model is not identified.</code></pre>
<pre class="r"><code>summary(fit3)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 158 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       100
## 
##   Number of observations                          1000
##   Number of missing patterns                        23
##   Sampling weights variable                     weight
## 
## Model Test User Model:
##                                                Standard      Scaled
##   Test Statistic                              17648.325   15780.877
##   Degrees of freedom                                150         150
##   P-value (Chi-square)                            0.000       0.000
##   Scaling correction factor                                   1.118
##     Yuan-Bentler correction (Mplus variant)                        
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.121    0.067   16.818    0.000
##     ptgi_3            1.122    0.071   15.865    0.000
##     ptgi_4            1.114    0.060   18.682    0.000
##     ptgi_5            1.173    0.076   15.459    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.081    0.065   16.548    0.000
##     ptsi_3            1.193    0.071   16.827    0.000
##     ptsi_4            1.217    0.057   21.503    0.000
##     ptsi_5            1.066    0.072   14.890    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi ~                                              
##     black             0.152    0.032    4.671    0.000
##     female            0.071    0.023    3.037    0.002
##     income           -0.006    0.043   -0.150    0.881
##   ptsi ~                                              
##     black             0.051    0.033    1.542    0.123
##     female            0.065    0.026    2.457    0.014
##     income           -0.088    0.048   -1.823    0.068
##   trstgov ~                                           
##     ptgi              0.010    0.059    0.162    0.871
##     ptsi              0.177    0.063    2.834    0.005
##     black             0.089    0.039    2.306    0.021
##     female           -0.048    0.030   -1.618    0.106
##     income            0.039    0.048    0.799    0.425
##     gop              -0.030    0.068   -0.443    0.658
##   trststate ~                                         
##     ptgi              0.052    0.058    0.897    0.370
##     ptsi              0.080    0.059    1.371    0.171
##     black             0.020    0.037    0.542    0.588
##     female           -0.096    0.030   -3.246    0.001
##     income            0.076    0.044    1.712    0.087
##     gop               0.113    0.084    1.348    0.178
##   trstloc ~                                           
##     ptgi              0.054    0.053    1.015    0.310
##     ptsi              0.003    0.063    0.040    0.968
##     black            -0.004    0.039   -0.111    0.912
##     female           -0.068    0.028   -2.406    0.016
##     income            0.140    0.044    3.140    0.002
##     gop               0.067    0.083    0.805    0.421
##   trstgen ~                                           
##     ptgi              0.140    0.083    1.686    0.092
##     ptsi             -0.112    0.092   -1.219    0.223
##     black            -0.175    0.051   -3.455    0.001
##     female           -0.112    0.041   -2.717    0.007
##     income            0.151    0.076    1.997    0.046
##     gop               0.039    0.101    0.387    0.699
##   trstgen2 ~                                          
##     ptgi              0.177    0.053    3.311    0.001
##     ptsi             -0.194    0.070   -2.794    0.005
##     black            -0.147    0.043   -3.417    0.001
##     female           -0.020    0.029   -0.678    0.498
##     income            0.140    0.045    3.118    0.002
##     gop               0.066    0.060    1.098    0.272
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.002    0.007    0.339    0.735
##     income           -0.014    0.004   -3.169    0.002
##   female ~~                                           
##     income           -0.014    0.006   -2.339    0.019
##   ptgi1 ~~                                            
##     ptsi1             0.014    0.003    5.402    0.000
##  .trstgov ~~                                          
##    .trststate         0.065    0.005   12.115    0.000
##    .trstgen           0.034    0.007    5.069    0.000
##  .trststate ~~                                        
##    .trstgen           0.041    0.006    6.533    0.000
##  .trstgov ~~                                          
##    .trstloc           0.061    0.004   13.683    0.000
##    .trstgen2          0.030    0.005    6.490    0.000
##  .trststate ~~                                        
##    .trstloc           0.077    0.005   16.327    0.000
##    .trstgen2          0.036    0.005    7.914    0.000
##  .trstloc ~~                                          
##    .trstgen           0.042    0.006    6.625    0.000
##    .trstgen2          0.044    0.004   10.139    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.072    0.006   12.553    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.623    0.013   47.805    0.000
##    .ptgi_2            0.506    0.016   32.560    0.000
##    .ptgi_3            0.508    0.017   29.438    0.000
##    .ptgi_4            0.589    0.015   39.634    0.000
##    .ptgi_5            0.439    0.016   27.175    0.000
##    .ptsi_1            0.304    0.017   18.276    0.000
##    .ptsi_2            0.278    0.015   19.144    0.000
##    .ptsi_3            0.379    0.016   24.295    0.000
##    .ptsi_4            0.303    0.017   18.097    0.000
##    .ptsi_5            0.364    0.016   23.095    0.000
##    .ptgi              0.483    0.028   17.567    0.000
##    .ptsi              0.323    0.034    9.403    0.000
##    .trstgov           0.320    0.044    7.219    0.000
##    .trststate         0.382    0.042    9.202    0.000
##    .trstloc           0.414    0.041    9.988    0.000
##    .trstgen           0.367    0.071    5.199    0.000
##    .trstgen2          0.482    0.042   11.512    0.000
##     black             0.122    0.014    8.969    0.000
##     female            0.514    0.021   24.046    0.000
##     income            0.423    0.012   34.618    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.041    0.004   10.883    0.000
##    .ptgi_2            0.037    0.004    8.973    0.000
##    .ptgi_3            0.064    0.007    9.552    0.000
##    .ptgi_4            0.043    0.005    8.778    0.000
##    .ptgi_5            0.048    0.004   10.949    0.000
##    .ptsi_1            0.055    0.006    8.789    0.000
##    .ptsi_2            0.037    0.004   10.365    0.000
##    .ptsi_3            0.040    0.005    8.451    0.000
##    .ptsi_4            0.039    0.005    7.652    0.000
##    .ptsi_5            0.054    0.004   13.370    0.000
##    .ptgi              0.065    0.003   20.861    0.000
##    .ptsi              0.072    0.004   16.216    0.000
##    .trstgov           0.107    0.005   19.911    0.000
##    .trststate         0.103    0.005   22.138    0.000
##    .trstloc           0.103    0.005   22.658    0.000
##    .trstgen           0.227    0.005   42.230    0.000
##    .trstgen2          0.091    0.004   20.768    0.000
##     black             0.107    0.010   10.420    0.000
##     female            0.250    0.001  419.282    0.000
##     income            0.083    0.004   20.114    0.000
##     ptgi1             0.049    0.004   10.986    0.000
##     ptsi1             0.053    0.006    8.527    0.000</code></pre>
<pre class="r"><code>sem4 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi ~ black + female + income
  ptsi ~ black + female + income
  trstgov ~ ptgi + ptsi + black + female + income + gop
  trststate ~ ptgi + ptsi + black + female + income + gop
  trstloc ~ ptgi + ptsi + black + female + income + gop
  trstgen ~ ptgi + ptsi + black + female + income + gop
  trstgen2 ~ ptgi + ptsi + black + female + income + gop
# residual correlations
  black ~~ female
  black ~~ income
  female ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

dat4$weight &lt;- as.numeric(dat4$weight)
dat4 &lt;- dat4 %&gt;% drop_na(weight)
dat4 &lt;- dat4 %&gt;% drop_na(gop)


fit4 &lt;- sem(sem4, missing = &#39;fiml&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat4)</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>summary(fit4)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 159 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       100
## 
##   Number of observations                          1000
##   Number of missing patterns                        25
## 
## Model Test User Model:
##                                                        
##   Test statistic                              18376.637
##   Degrees of freedom                                150
##   P-value (Chi-square)                            0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.202    0.058   20.653    0.000
##     ptgi_3            1.185    0.064   18.552    0.000
##     ptgi_4            1.150    0.059   19.451    0.000
##     ptgi_5            1.189    0.064   18.476    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.076    0.050   21.608    0.000
##     ptsi_3            1.108    0.052   21.210    0.000
##     ptsi_4            1.222    0.051   24.082    0.000
##     ptsi_5            0.977    0.053   18.356    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi ~                                              
##     black             0.137    0.031    4.377    0.000
##     female            0.080    0.020    3.964    0.000
##     income           -0.079    0.038   -2.083    0.037
##   ptsi ~                                              
##     black             0.093    0.030    3.079    0.002
##     female            0.045    0.019    2.332    0.020
##     income           -0.160    0.036   -4.412    0.000
##   trstgov ~                                           
##     ptgi              0.042    0.042    1.002    0.317
##     ptsi              0.108    0.044    2.472    0.013
##     black             0.006    0.032    0.175    0.861
##     female           -0.059    0.020   -2.940    0.003
##     income            0.113    0.038    3.011    0.003
##     gop              -0.277    0.020  -13.574    0.000
##   trststate ~                                         
##     ptgi              0.023    0.042    0.544    0.587
##     ptsi              0.051    0.044    1.170    0.242
##     black            -0.002    0.033   -0.052    0.959
##     female           -0.067    0.020   -3.284    0.001
##     income            0.102    0.038    2.648    0.008
##     gop              -0.117    0.021   -5.594    0.000
##   trstloc ~                                           
##     ptgi              0.027    0.042    0.634    0.526
##     ptsi             -0.021    0.044   -0.482    0.630
##     black            -0.046    0.033   -1.417    0.156
##     female           -0.090    0.020   -4.452    0.000
##     income            0.131    0.038    3.431    0.001
##     gop              -0.129    0.021   -6.211    0.000
##   trstgen ~                                           
##     ptgi              0.008    0.067    0.122    0.903
##     ptsi             -0.158    0.068   -2.312    0.021
##     black            -0.146    0.050   -2.900    0.004
##     female           -0.029    0.032   -0.917    0.359
##     income            0.234    0.060    3.926    0.000
##     gop              -0.119    0.032   -3.687    0.000
##   trstgen2 ~                                          
##     ptgi              0.067    0.042    1.591    0.112
##     ptsi             -0.214    0.043   -4.972    0.000
##     black            -0.089    0.032   -2.784    0.005
##     female           -0.049    0.020   -2.504    0.012
##     income            0.179    0.037    4.769    0.000
##     gop              -0.071    0.020   -3.532    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female           -0.009    0.005   -1.674    0.094
##     income           -0.017    0.003   -5.520    0.000
##   female ~~                                           
##     income           -0.016    0.005   -3.432    0.001
##   ptgi1 ~~                                            
##     ptsi1             0.012    0.002    5.071    0.000
##  .trstgov ~~                                          
##    .trststate         0.052    0.003   15.062    0.000
##    .trstgen           0.028    0.005    5.826    0.000
##  .trststate ~~                                        
##    .trstgen           0.027    0.005    5.485    0.000
##  .trstgov ~~                                          
##    .trstloc           0.047    0.003   14.020    0.000
##    .trstgen2          0.025    0.003    8.288    0.000
##  .trststate ~~                                        
##    .trstloc           0.063    0.004   17.088    0.000
##    .trstgen2          0.029    0.003    9.185    0.000
##  .trstloc ~~                                          
##    .trstgen           0.037    0.005    7.547    0.000
##    .trstgen2          0.036    0.003   11.349    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.076    0.005   14.502    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.587    0.012   49.659    0.000
##    .ptgi_2            0.480    0.012   39.495    0.000
##    .ptgi_3            0.509    0.013   38.317    0.000
##    .ptgi_4            0.599    0.012   49.426    0.000
##    .ptgi_5            0.434    0.013   33.425    0.000
##    .ptsi_1            0.261    0.011   22.706    0.000
##    .ptsi_2            0.259    0.012   22.319    0.000
##    .ptsi_3            0.324    0.012   26.733    0.000
##    .ptsi_4            0.284    0.012   23.300    0.000
##    .ptsi_5            0.339    0.012   27.528    0.000
##    .ptgi              0.495    0.024   20.458    0.000
##    .ptsi              0.326    0.023   14.047    0.000
##    .trstgov           0.453    0.035   13.115    0.000
##    .trststate         0.457    0.035   12.983    0.000
##    .trstloc           0.541    0.035   15.462    0.000
##    .trstgen           0.483    0.055    8.780    0.000
##    .trstgen2          0.617    0.034   17.877    0.000
##     black             0.124    0.010   11.898    0.000
##     female            0.538    0.016   34.125    0.000
##     income            0.421    0.009   45.607    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.053    0.003   16.650    0.000
##    .ptgi_2            0.036    0.003   13.493    0.000
##    .ptgi_3            0.059    0.004   16.052    0.000
##    .ptgi_4            0.042    0.003   14.807    0.000
##    .ptgi_5            0.053    0.003   15.520    0.000
##    .ptsi_1            0.043    0.003   16.352    0.000
##    .ptsi_2            0.037    0.002   15.085    0.000
##    .ptsi_3            0.042    0.003   15.437    0.000
##    .ptsi_4            0.028    0.002   12.211    0.000
##    .ptsi_5            0.061    0.003   17.361    0.000
##    .ptgi              0.074    0.004   19.380    0.000
##    .ptsi              0.069    0.004   19.344    0.000
##    .trstgov           0.089    0.004   21.733    0.000
##    .trststate         0.095    0.004   21.934    0.000
##    .trstloc           0.093    0.004   21.781    0.000
##    .trstgen           0.234    0.011   22.275    0.000
##    .trstgen2          0.089    0.004   21.669    0.000
##     black             0.109    0.005   22.361    0.000
##     female            0.249    0.011   22.361    0.000
##     income            0.079    0.004   21.419    0.000
##     ptgi1             0.052    0.005   10.500    0.000
##     ptsi1             0.056    0.005   11.618    0.000</code></pre>
<pre class="r"><code>sem5 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  ptgi ~ black + female + income
  ptsi ~ black + female + income
  trstgov ~ ptgi + ptsi + black + female + income + gop
  trststate ~ ptgi + ptsi + black + female + income + gop
  trstloc ~ ptgi + ptsi + black + female + income + gop
  trstgen ~ ptgi + ptsi + black + female + income + gop
  trstgen2 ~ ptgi + ptsi + black + female + income + gop
# residual correlations
  black ~~ female
  black ~~ income
  female ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

fit5 &lt;- sem(sem5, missing = &#39;fiml&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat5)</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [black female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.

## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>summary(fit5)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 175 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       100
## 
##   Number of observations                          1000
##   Number of missing patterns                        22
## 
## Model Test User Model:
##                                                        
##   Test statistic                              19956.003
##   Degrees of freedom                                150
##   P-value (Chi-square)                            0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.219    0.064   19.002    0.000
##     ptgi_3            1.343    0.074   18.158    0.000
##     ptgi_4            1.245    0.068   18.401    0.000
##     ptgi_5            1.342    0.073   18.288    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.078    0.053   20.511    0.000
##     ptsi_3            1.145    0.058   19.714    0.000
##     ptsi_4            1.265    0.056   22.789    0.000
##     ptsi_5            0.939    0.055   16.915    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi ~                                              
##     black             0.136    0.036    3.818    0.000
##     female            0.085    0.020    4.342    0.000
##     income           -0.114    0.035   -3.218    0.001
##   ptsi ~                                              
##     black             0.035    0.034    1.033    0.302
##     female            0.080    0.019    4.276    0.000
##     income           -0.150    0.034   -4.451    0.000
##   trstgov ~                                           
##     ptgi              0.079    0.040    1.989    0.047
##     ptsi             -0.025    0.042   -0.592    0.554
##     black             0.059    0.035    1.689    0.091
##     female           -0.014    0.019   -0.738    0.461
##     income            0.086    0.034    2.562    0.010
##     gop              -0.364    0.019  -19.311    0.000
##   trststate ~                                         
##     ptgi              0.099    0.043    2.294    0.022
##     ptsi             -0.118    0.046   -2.595    0.009
##     black             0.041    0.038    1.063    0.288
##     female           -0.014    0.021   -0.662    0.508
##     income            0.066    0.037    1.767    0.077
##     gop              -0.194    0.021   -9.303    0.000
##   trstloc ~                                           
##     ptgi              0.079    0.042    1.852    0.064
##     ptsi             -0.165    0.045   -3.664    0.000
##     black            -0.048    0.038   -1.265    0.206
##     female           -0.033    0.021   -1.584    0.113
##     income            0.081    0.037    2.176    0.030
##     gop              -0.153    0.021   -7.436    0.000
##   trstgen ~                                           
##     ptgi              0.076    0.067    1.136    0.256
##     ptsi             -0.186    0.070   -2.651    0.008
##     black            -0.099    0.058   -1.694    0.090
##     female           -0.008    0.032   -0.264    0.792
##     income            0.242    0.058    4.202    0.000
##     gop              -0.102    0.032   -3.171    0.002
##   trstgen2 ~                                          
##     ptgi              0.081    0.041    1.957    0.050
##     ptsi             -0.180    0.044   -4.136    0.000
##     black            -0.112    0.037   -3.074    0.002
##     female            0.005    0.020    0.267    0.790
##     income            0.201    0.036    5.560    0.000
##     gop              -0.060    0.020   -2.986    0.003
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     female            0.005    0.004    1.209    0.227
##     income           -0.014    0.003   -5.272    0.000
##   female ~~                                           
##     income           -0.031    0.005   -6.443    0.000
##   ptgi1 ~~                                            
##     ptsi1             0.012    0.002    5.993    0.000
##  .trstgov ~~                                          
##    .trststate         0.053    0.003   16.309    0.000
##    .trstgen           0.026    0.004    5.972    0.000
##  .trststate ~~                                        
##    .trstgen           0.033    0.005    6.870    0.000
##  .trstgov ~~                                          
##    .trstloc           0.045    0.003   14.546    0.000
##    .trstgen2          0.025    0.003    8.847    0.000
##  .trststate ~~                                        
##    .trstloc           0.062    0.004   17.079    0.000
##    .trstgen2          0.033    0.003   10.526    0.000
##  .trstloc ~~                                          
##    .trstgen           0.032    0.005    6.714    0.000
##    .trstgen2          0.038    0.003   11.965    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.073    0.005   14.327    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.606    0.011   54.642    0.000
##    .ptgi_2            0.462    0.012   39.605    0.000
##    .ptgi_3            0.481    0.013   36.160    0.000
##    .ptgi_4            0.580    0.012   47.361    0.000
##    .ptgi_5            0.395    0.013   30.835    0.000
##    .ptsi_1            0.260    0.011   23.103    0.000
##    .ptsi_2            0.228    0.011   21.002    0.000
##    .ptsi_3            0.307    0.012   25.493    0.000
##    .ptsi_4            0.259    0.012   21.686    0.000
##    .ptsi_5            0.334    0.012   28.577    0.000
##    .ptgi              0.499    0.023   21.404    0.000
##    .ptsi              0.299    0.022   13.500    0.000
##    .trstgov           0.488    0.032   15.187    0.000
##    .trststate         0.483    0.035   13.648    0.000
##    .trstloc           0.551    0.035   15.700    0.000
##    .trstgen           0.355    0.055    6.508    0.000
##    .trstgen2          0.533    0.034   15.691    0.000
##     black             0.082    0.009    9.451    0.000
##     female            0.546    0.016   34.679    0.000
##     income            0.443    0.010   46.306    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.052    0.003   17.076    0.000
##    .ptgi_2            0.040    0.003   14.818    0.000
##    .ptgi_3            0.058    0.004   15.686    0.000
##    .ptgi_4            0.048    0.003   15.459    0.000
##    .ptgi_5            0.048    0.003   14.749    0.000
##    .ptsi_1            0.047    0.003   16.798    0.000
##    .ptsi_2            0.032    0.002   14.629    0.000
##    .ptsi_3            0.045    0.003   15.755    0.000
##    .ptsi_4            0.029    0.002   12.048    0.000
##    .ptsi_5            0.061    0.003   17.787    0.000
##    .ptgi              0.070    0.004   19.631    0.000
##    .ptsi              0.064    0.003   19.612    0.000
##    .trstgov           0.077    0.004   22.035    0.000
##    .trststate         0.094    0.004   21.987    0.000
##    .trstloc           0.092    0.004   21.867    0.000
##    .trstgen           0.226    0.010   22.233    0.000
##    .trstgen2          0.088    0.004   21.945    0.000
##     black             0.075    0.003   22.361    0.000
##     female            0.248    0.011   22.361    0.000
##     income            0.084    0.004   21.356    0.000
##     ptgi1             0.044    0.004   10.061    0.000
##     ptsi1             0.051    0.005   11.037    0.000</code></pre>
<pre class="r"><code>race1 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  trstgov ~ ptgi + ptsi + gop + female + income
  trststate ~ ptgi + ptsi + gop + female + income
  trstgen ~ ptgi + ptsi + gop + female + income
# residual correlations
  female ~~ income
  gop ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

race2 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  trstgov ~ ptgi + ptsi + gop + female + income
  trststate ~ ptgi + ptsi + gop + female + income
  trstloc ~ ptgi + ptsi + gop + female + income
  trstgen ~ ptgi + ptsi + gop + female + income
  trstgen2 ~ ptgi + ptsi + gop + female + income
# residual correlations
  female ~~ income
  gop ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

# Constraining and Loosening across groups to test for differences

# study I
fit_r1.1 &lt;- sem(race1, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat1)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 263 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 25 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_r1.2 &lt;- sem(race1, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat1, group.equal = &quot;loadings&quot;)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 263 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 25 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_r1.3 &lt;- sem(race1, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat1, group.equal = c(&quot;intercepts&quot;, &quot;loadings&quot;))</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 263 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 25 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>lavTestLRT(fit_r1.1, fit_r1.2, fit_r1.3) # the groups are statistically significantly different</code></pre>
<pre><code>## 
## Chi-Squared Difference Test
## 
##           Df    AIC    BIC Chisq Chisq diff    RMSEA Df diff Pr(&gt;Chisq)    
## fit_r1.1 242 4295.3 4943.5 46387                                           
## fit_r1.2 250 4290.8 4897.8 46398     11.473 0.026177       8     0.1763    
## fit_r1.3 264 4315.4 4850.4 46451     52.603 0.065974      14  2.222e-06 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># pool all YouGov surveys 
dat_mg &lt;- do.call(&quot;rbind&quot;, list(dat2, dat3, dat4, dat5))

# study II
fit_r1 &lt;- sem(race2, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 849 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 130 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_r2 &lt;- sem(race2, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg, group.equal = &quot;loadings&quot;)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 849 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 130 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_r3 &lt;- sem(race2, missing = &#39;fiml&#39;, group = &#39;black&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg, group.equal = c(&quot;intercepts&quot;, &quot;loadings&quot;))</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 849 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 130 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop female income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>lavTestLRT(fit_r1, fit_r2, fit_r3) # the groups are statistically significantly different</code></pre>
<pre><code>## 
## Chi-Squared Difference Test
## 
##         Df   AIC   BIC  Chisq Chisq diff    RMSEA Df diff Pr(&gt;Chisq)    
## fit_r1 282 18838 19848 110378                                           
## fit_r2 290 18834 19796 110389     11.332 0.016605       8     0.1836    
## fit_r3 306 19071 19937 110658    269.371 0.102390      16     &lt;2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>summary(fit_r1)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 226 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       168
## 
##   Number of observations per group:               Used       Total
##     0                                             2710        3559
##     1                                              311         441
##   Number of missing patterns per group:                           
##     0                                               18            
##     1                                               13            
## 
## Model Test User Model:
##                                                         
##   Test statistic                              110377.569
##   Degrees of freedom                                 282
##   P-value (Chi-square)                             0.000
##   Test statistic for each group:
##     0                                         98986.201
##     1                                         11391.368
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## 
## Group 1 [0]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.179    0.030   39.620    0.000
##     ptgi_3            1.205    0.034   35.701    0.000
##     ptgi_4            1.173    0.032   37.141    0.000
##     ptgi_5            1.196    0.033   35.915    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.102    0.028   39.910    0.000
##     ptsi_3            1.187    0.030   39.806    0.000
##     ptsi_4            1.230    0.028   43.324    0.000
##     ptsi_5            1.007    0.029   34.245    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   trstgov ~                                           
##     ptgi              0.051    0.022    2.273    0.023
##     ptsi              0.058    0.023    2.504    0.012
##     gop              -0.230    0.013  -18.212    0.000
##     female           -0.019    0.012   -1.528    0.127
##     income            0.098    0.022    4.541    0.000
##   trststate ~                                         
##     ptgi              0.068    0.023    2.896    0.004
##     ptsi             -0.031    0.024   -1.313    0.189
##     gop              -0.092    0.013   -7.002    0.000
##     female           -0.026    0.013   -2.012    0.044
##     income            0.095    0.023    4.208    0.000
##   trstloc ~                                           
##     ptgi              0.069    0.023    2.955    0.003
##     ptsi             -0.069    0.024   -2.897    0.004
##     gop              -0.075    0.013   -5.667    0.000
##     female           -0.035    0.013   -2.751    0.006
##     income            0.134    0.023    5.928    0.000
##   trstgen ~                                           
##     ptgi              0.036    0.035    1.014    0.311
##     ptsi             -0.185    0.036   -5.114    0.000
##     gop              -0.074    0.020   -3.677    0.000
##     female           -0.042    0.019   -2.185    0.029
##     income            0.273    0.034    7.943    0.000
##   trstgen2 ~                                          
##     ptgi              0.097    0.022    4.470    0.000
##     ptsi             -0.224    0.022  -10.050    0.000
##     gop              -0.037    0.012   -3.037    0.002
##     female           -0.010    0.012   -0.845    0.398
##     income            0.196    0.021    9.293    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   female ~~                                           
##     income           -0.023    0.003   -7.770    0.000
##   gop ~~                                              
##     income           -0.003    0.003   -1.113    0.266
##   ptgi1 ~~                                            
##     ptsi1             0.013    0.001   10.881    0.000
##  .trstgov ~~                                          
##    .trststate         0.056    0.002   25.837    0.000
##    .trstgen           0.025    0.003    8.636    0.000
##  .trststate ~~                                        
##    .trstgen           0.030    0.003   10.087    0.000
##  .trstgov ~~                                          
##    .trstloc           0.050    0.002   23.976    0.000
##    .trstgen2          0.022    0.002   12.487    0.000
##  .trststate ~~                                        
##    .trstloc           0.068    0.002   28.970    0.000
##    .trstgen2          0.028    0.002   14.949    0.000
##  .trstloc ~~                                          
##    .trstgen           0.036    0.003   11.887    0.000
##    .trstgen2          0.035    0.002   17.896    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.075    0.003   24.135    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.587    0.006   98.071    0.000
##    .ptgi_2            0.458    0.006   73.391    0.000
##    .ptgi_3            0.469    0.007   67.203    0.000
##    .ptgi_4            0.571    0.006   88.400    0.000
##    .ptgi_5            0.398    0.007   59.408    0.000
##    .ptsi_1            0.277    0.006   45.428    0.000
##    .ptsi_2            0.256    0.006   41.839    0.000
##    .ptsi_3            0.337    0.007   51.447    0.000
##    .ptsi_4            0.281    0.006   43.459    0.000
##    .ptsi_5            0.348    0.007   53.452    0.000
##    .trstgov           0.368    0.018   19.916    0.000
##    .trststate         0.401    0.019   20.770    0.000
##    .trstloc           0.452    0.019   23.509    0.000
##    .trstgen           0.384    0.029   13.110    0.000
##    .trstgen2          0.534    0.018   29.730    0.000
##     gop               0.349    0.009   38.153    0.000
##     female            0.559    0.010   58.615    0.000
##     income            0.448    0.006   77.404    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.047    0.002   31.032    0.000
##    .ptgi_2            0.036    0.001   26.312    0.000
##    .ptgi_3            0.060    0.002   30.455    0.000
##    .ptgi_4            0.044    0.002   28.355    0.000
##    .ptgi_5            0.050    0.002   29.052    0.000
##    .ptsi_1            0.047    0.001   31.383    0.000
##    .ptsi_2            0.036    0.001   28.171    0.000
##    .ptsi_3            0.040    0.001   27.935    0.000
##    .ptsi_4            0.032    0.001   24.669    0.000
##    .ptsi_5            0.060    0.002   32.717    0.000
##    .trstgov           0.091    0.003   36.289    0.000
##    .trststate         0.100    0.003   36.374    0.000
##    .trstloc           0.099    0.003   36.202    0.000
##    .trstgen           0.232    0.006   36.719    0.000
##    .trstgen2          0.087    0.002   36.397    0.000
##     gop               0.227    0.006   36.810    0.000
##     female            0.247    0.007   36.810    0.000
##     income            0.083    0.002   35.132    0.000
##     ptgi1             0.050    0.002   20.417    0.000
##     ptsi1             0.054    0.003   21.239    0.000
## 
## 
## Group 2 [1]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.104    0.098   11.215    0.000
##     ptgi_3            1.259    0.106   11.830    0.000
##     ptgi_4            1.305    0.103   12.726    0.000
##     ptgi_5            1.169    0.108   10.801    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.135    0.090   12.586    0.000
##     ptsi_3            1.029    0.096   10.758    0.000
##     ptsi_4            1.237    0.095   13.040    0.000
##     ptsi_5            0.957    0.093   10.284    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   trstgov ~                                           
##     ptgi             -0.193    0.075   -2.588    0.010
##     ptsi              0.106    0.075    1.413    0.158
##     gop              -0.058    0.070   -0.832    0.405
##     female           -0.046    0.039   -1.190    0.234
##     income            0.098    0.077    1.275    0.202
##   trststate ~                                         
##     ptgi             -0.057    0.071   -0.812    0.417
##     ptsi              0.010    0.070    0.138    0.890
##     gop               0.011    0.066    0.162    0.872
##     female           -0.022    0.037   -0.610    0.542
##     income            0.111    0.072    1.536    0.125
##   trstloc ~                                           
##     ptgi             -0.133    0.068   -1.974    0.048
##     ptsi              0.027    0.067    0.398    0.690
##     gop              -0.067    0.063   -1.067    0.286
##     female           -0.079    0.035   -2.261    0.024
##     income            0.126    0.069    1.821    0.069
##   trstgen ~                                           
##     ptgi             -0.062    0.098   -0.631    0.528
##     ptsi             -0.048    0.098   -0.488    0.626
##     gop               0.069    0.088    0.785    0.432
##     female           -0.055    0.051   -1.078    0.281
##     income            0.074    0.102    0.724    0.469
##   trstgen2 ~                                          
##     ptgi             -0.117    0.068   -1.713    0.087
##     ptsi             -0.090    0.068   -1.325    0.185
##     gop              -0.049    0.062   -0.787    0.431
##     female           -0.034    0.035   -0.948    0.343
##     income            0.046    0.070    0.663    0.507
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   female ~~                                           
##     income           -0.020    0.008   -2.613    0.009
##   gop ~~                                              
##     income            0.001    0.004    0.132    0.895
##   ptgi1 ~~                                            
##     ptsi1             0.011    0.003    3.359    0.001
##  .trstgov ~~                                          
##    .trststate         0.074    0.007    9.963    0.000
##    .trstgen           0.045    0.009    5.037    0.000
##  .trststate ~~                                        
##    .trstgen           0.037    0.008    4.456    0.000
##  .trstgov ~~                                          
##    .trstloc           0.072    0.007   10.077    0.000
##    .trstgen2          0.048    0.006    7.356    0.000
##  .trststate ~~                                        
##    .trstloc           0.072    0.007   10.525    0.000
##    .trstgen2          0.038    0.006    6.420    0.000
##  .trstloc ~~                                          
##    .trstgen           0.037    0.008    4.645    0.000
##    .trstgen2          0.045    0.006    7.635    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.060    0.008    7.099    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.713    0.018   39.212    0.000
##    .ptgi_2            0.628    0.018   34.789    0.000
##    .ptgi_3            0.646    0.019   33.549    0.000
##    .ptgi_4            0.692    0.018   37.547    0.000
##    .ptgi_5            0.612    0.019   31.666    0.000
##    .ptsi_1            0.361    0.018   19.507    0.000
##    .ptsi_2            0.319    0.019   17.145    0.000
##    .ptsi_3            0.435    0.020   21.768    0.000
##    .ptsi_4            0.365    0.019   18.781    0.000
##    .ptsi_5            0.392    0.019   20.160    0.000
##    .trstgov           0.598    0.063    9.543    0.000
##    .trststate         0.484    0.059    8.180    0.000
##    .trstloc           0.580    0.056   10.283    0.000
##    .trstgen           0.327    0.082    3.996    0.000
##    .trstgen2          0.612    0.057   10.680    0.000
##     gop               0.093    0.016    5.655    0.000
##     female            0.492    0.028   17.354    0.000
##     income            0.297    0.015   19.509    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.057    0.005   11.094    0.000
##    .ptgi_2            0.046    0.004   10.318    0.000
##    .ptgi_3            0.043    0.005    9.536    0.000
##    .ptgi_4            0.028    0.004    7.670    0.000
##    .ptgi_5            0.054    0.005   10.289    0.000
##    .ptsi_1            0.053    0.005   10.524    0.000
##    .ptsi_2            0.039    0.004    8.952    0.000
##    .ptsi_3            0.068    0.006   10.849    0.000
##    .ptsi_4            0.036    0.005    7.988    0.000
##    .ptsi_5            0.069    0.006   11.103    0.000
##    .trstgov           0.107    0.009   11.949    0.000
##    .trststate         0.098    0.008   12.241    0.000
##    .trstloc           0.089    0.007   12.115    0.000
##    .trstgen           0.195    0.016   12.469    0.000
##    .trstgen2          0.092    0.008   12.230    0.000
##     gop               0.085    0.007   12.470    0.000
##     female            0.250    0.020   12.470    0.000
##     income            0.067    0.006   12.027    0.000
##     ptgi1             0.045    0.007    6.284    0.000
##     ptsi1             0.053    0.008    6.765    0.000</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Pooled Studies II-V&#39; = fit_r1),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = 0.05),
             gof_omit = &#39;AIC|BIC|F|Log.Lik.&#39;,
             escape = F)</code></pre>
<pre><code>## Warning: There are duplicate term names in the table.
##   The `shape` argument of the `modelsummary` function can be used to print
##   related terms together. The `group_map` argument can be used to reorder,
##   subset, and rename group identifiers. See `?modelsummary` for details.
##   You can find the group identifier to use in the `shape` argument by
##   calling `get_estimates()` on one of your models. Candidates include:
##   component, group, s.value</code></pre>
<pre class="r"><code>## Gender

woman2 &lt;-&#39;
# factor construction
  ptgi1 =~ ptgi_1 + ptgi_2 + ptgi_3 + ptgi_4 + ptgi_5
  ptsi1 =~ ptsi_1 + ptsi_2 + ptsi_3 + ptsi_4 + ptsi_5
# models
  trstgov ~ ptgi + ptsi + gop + black + income
  trststate ~ ptgi + ptsi + gop + black + income
  trstloc ~ ptgi + ptsi + gop + black + income
  trstgen ~ ptgi + ptsi + gop + black + income
  trstgen2 ~ ptgi + ptsi + gop + black + income
# residual correlations
  black ~~ income
  gop ~~ income
  ptgi1 ~~ ptsi1
  trstgov ~~ trststate
  trstgov ~~ trstgen
  trststate ~~ trstgen
&#39;

# study II
fit_w1 &lt;- sem(woman2, missing = &#39;fiml&#39;, group = &#39;female&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 513 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 466 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop black income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_w2 &lt;- sem(woman2, missing = &#39;fiml&#39;, group = &#39;female&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg, group.equal = &quot;loadings&quot;)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 513 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 466 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop black income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>fit_w3 &lt;- sem(woman2, missing = &#39;fiml&#39;, group = &#39;female&#39;, #sampling.weights = &#39;weight&#39;,
            data = dat_mg, group.equal = c(&quot;intercepts&quot;, &quot;loadings&quot;))</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 513 cases were deleted in group 1 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 466 cases were deleted in group 0 due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre><code>## Warning in lav_partable_vnames(FLAT, &quot;ov.x&quot;, warn = TRUE): lavaan WARNING:
##     model syntax contains variance/covariance/intercept formulas
##     involving (an) exogenous variable(s): [gop black income]; These
##     variables will now be treated as random introducing additional
##     free parameters. If you wish to treat those variables as fixed,
##     remove these formulas from the model syntax. Otherwise, consider
##     adding the fixed.x = FALSE option.</code></pre>
<pre><code>## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.

## Warning in lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]], : lavaan WARNING:
##     The smallest eigenvalue of the EM estimated variance-covariance
##     matrix (Sigma) is smaller than 1e-05; this may cause numerical
##     instabilities; interpret the results with caution.</code></pre>
<pre class="r"><code>lavTestLRT(fit_w1, fit_w2, fit_w3) # the groups are statistically significantly different</code></pre>
<pre><code>## 
## Chi-Squared Difference Test
## 
##         Df   AIC   BIC  Chisq Chisq diff    RMSEA Df diff Pr(&gt;Chisq)    
## fit_w1 282 15818 16828 110460                                           
## fit_w2 290 15817 16779 110475     15.282 0.024549       8    0.05389 .  
## fit_w3 306 15982 16848 110672    196.625 0.086451      16    &lt; 2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>summary(fit_w1)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 217 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       168
## 
##   Number of observations per group:               Used       Total
##     1                                             1668        2181
##     0                                             1353        1819
##   Number of missing patterns per group:                           
##     1                                               14            
##     0                                               20            
## 
## Model Test User Model:
##                                                         
##   Test statistic                              110460.217
##   Degrees of freedom                                 282
##   P-value (Chi-square)                             0.000
##   Test statistic for each group:
##     1                                         61146.147
##     0                                         49314.069
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## 
## Group 1 [1]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.217    0.041   29.601    0.000
##     ptgi_3            1.263    0.046   27.679    0.000
##     ptgi_4            1.137    0.041   27.472    0.000
##     ptgi_5            1.221    0.046   26.277    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.051    0.035   29.656    0.000
##     ptsi_3            1.103    0.038   29.181    0.000
##     ptsi_4            1.216    0.036   33.559    0.000
##     ptsi_5            0.963    0.037   25.873    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   trstgov ~                                           
##     ptgi             -0.010    0.028   -0.364    0.716
##     ptsi              0.002    0.027    0.065    0.948
##     gop              -0.195    0.017  -11.690    0.000
##     black             0.080    0.027    3.011    0.003
##     income            0.084    0.028    2.955    0.003
##   trststate ~                                         
##     ptgi              0.018    0.029    0.601    0.548
##     ptsi             -0.091    0.028   -3.238    0.001
##     gop              -0.046    0.017   -2.701    0.007
##     black             0.045    0.027    1.670    0.095
##     income            0.076    0.029    2.613    0.009
##   trstloc ~                                           
##     ptgi              0.021    0.029    0.721    0.471
##     ptsi             -0.109    0.028   -3.885    0.000
##     gop              -0.050    0.017   -2.956    0.003
##     black            -0.011    0.027   -0.400    0.689
##     income            0.102    0.029    3.556    0.000
##   trstgen ~                                           
##     ptgi             -0.010    0.045   -0.224    0.822
##     ptsi             -0.240    0.043   -5.568    0.000
##     gop              -0.025    0.026   -0.965    0.334
##     black            -0.116    0.042   -2.767    0.006
##     income            0.207    0.045    4.647    0.000
##   trstgen2 ~                                          
##     ptgi              0.060    0.027    2.190    0.028
##     ptsi             -0.270    0.027  -10.155    0.000
##     gop              -0.021    0.016   -1.277    0.202
##     black            -0.070    0.026   -2.740    0.006
##     income            0.199    0.027    7.268    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     income           -0.013    0.002   -6.307    0.000
##   gop ~~                                              
##     income           -0.004    0.003   -1.265    0.206
##   ptgi1 ~~                                            
##     ptsi1             0.009    0.002    5.806    0.000
##  .trstgov ~~                                          
##    .trststate         0.053    0.003   20.331    0.000
##    .trstgen           0.023    0.004    6.424    0.000
##  .trststate ~~                                        
##    .trstgen           0.026    0.004    7.140    0.000
##  .trstgov ~~                                          
##    .trstloc           0.050    0.003   19.579    0.000
##    .trstgen2          0.023    0.002   10.340    0.000
##  .trststate ~~                                        
##    .trstloc           0.067    0.003   23.589    0.000
##    .trstgen2          0.028    0.002   11.949    0.000
##  .trstloc ~~                                          
##    .trstgen           0.034    0.004    9.371    0.000
##    .trstgen2          0.034    0.002   14.624    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.070    0.004   18.533    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.634    0.008   84.295    0.000
##    .ptgi_2            0.493    0.008   61.581    0.000
##    .ptgi_3            0.521    0.009   58.574    0.000
##    .ptgi_4            0.643    0.008   81.334    0.000
##    .ptgi_5            0.460    0.009   52.470    0.000
##    .ptsi_1            0.321    0.008   39.668    0.000
##    .ptsi_2            0.296    0.008   36.618    0.000
##    .ptsi_3            0.401    0.009   46.818    0.000
##    .ptsi_4            0.328    0.009   38.493    0.000
##    .ptsi_5            0.389    0.009   45.781    0.000
##    .trstgov           0.396    0.023   17.075    0.000
##    .trststate         0.416    0.024   17.464    0.000
##    .trstloc           0.462    0.024   19.564    0.000
##    .trstgen           0.396    0.036   10.880    0.000
##    .trstgen2          0.553    0.022   24.713    0.000
##     gop               0.299    0.011   26.645    0.000
##     black             0.092    0.007   12.979    0.000
##     income            0.394    0.007   55.347    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.048    0.002   24.362    0.000
##    .ptgi_2            0.038    0.002   20.490    0.000
##    .ptgi_3            0.058    0.002   23.036    0.000
##    .ptgi_4            0.044    0.002   22.543    0.000
##    .ptgi_5            0.059    0.003   23.355    0.000
##    .ptsi_1            0.051    0.002   24.147    0.000
##    .ptsi_2            0.044    0.002   22.614    0.000
##    .ptsi_3            0.051    0.002   22.958    0.000
##    .ptsi_4            0.034    0.002   18.145    0.000
##    .ptsi_5            0.066    0.003   25.585    0.000
##    .trstgov           0.089    0.003   28.520    0.000
##    .trststate         0.094    0.003   28.610    0.000
##    .trstloc           0.093    0.003   28.524    0.000
##    .trstgen           0.223    0.008   28.839    0.000
##    .trstgen2          0.084    0.003   28.576    0.000
##     gop               0.209    0.007   28.879    0.000
##     black             0.083    0.003   28.879    0.000
##     income            0.077    0.003   27.528    0.000
##     ptgi1             0.047    0.003   15.509    0.000
##     ptsi1             0.059    0.004   16.592    0.000
## 
## 
## Group 2 [0]:
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   ptgi1 =~                                            
##     ptgi_1            1.000                           
##     ptgi_2            1.181    0.041   29.127    0.000
##     ptgi_3            1.181    0.046   25.818    0.000
##     ptgi_4            1.195    0.043   28.078    0.000
##     ptgi_5            1.197    0.044   27.220    0.000
##   ptsi1 =~                                            
##     ptsi_1            1.000                           
##     ptsi_2            1.176    0.040   29.162    0.000
##     ptsi_3            1.235    0.043   28.466    0.000
##     ptsi_4            1.259    0.042   29.931    0.000
##     ptsi_5            1.039    0.043   24.010    0.000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   trstgov ~                                           
##     ptgi              0.047    0.033    1.438    0.151
##     ptsi              0.168    0.036    4.641    0.000
##     gop              -0.247    0.019  -13.243    0.000
##     black             0.089    0.029    3.065    0.002
##     income            0.112    0.031    3.608    0.000
##   trststate ~                                         
##     ptgi              0.076    0.034    2.232    0.026
##     ptsi              0.079    0.037    2.126    0.033
##     gop              -0.130    0.019   -6.747    0.000
##     black             0.014    0.030    0.476    0.634
##     income            0.119    0.032    3.706    0.000
##   trstloc ~                                           
##     ptgi              0.066    0.034    1.956    0.050
##     ptsi              0.015    0.037    0.417    0.677
##     gop              -0.092    0.019   -4.794    0.000
##     black             0.022    0.030    0.751    0.453
##     income            0.163    0.032    5.079    0.000
##   trstgen ~                                           
##     ptgi              0.031    0.050    0.611    0.541
##     ptsi             -0.050    0.054   -0.915    0.360
##     gop              -0.108    0.028   -3.786    0.000
##     black            -0.136    0.043   -3.133    0.002
##     income            0.309    0.048    6.490    0.000
##   trstgen2 ~                                          
##     ptgi              0.080    0.032    2.524    0.012
##     ptsi             -0.117    0.034   -3.405    0.001
##     gop              -0.047    0.018   -2.624    0.009
##     black            -0.079    0.028   -2.861    0.004
##     income            0.162    0.030    5.410    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   black ~~                                            
##     income           -0.017    0.003   -6.137    0.000
##   gop ~~                                              
##     income           -0.001    0.004   -0.258    0.796
##   ptgi1 ~~                                            
##     ptsi1             0.016    0.002    9.764    0.000
##  .trstgov ~~                                          
##    .trststate         0.061    0.003   18.694    0.000
##    .trstgen           0.030    0.004    7.022    0.000
##  .trststate ~~                                        
##    .trstgen           0.035    0.004    7.969    0.000
##  .trstgov ~~                                          
##    .trstloc           0.054    0.003   17.026    0.000
##    .trstgen2          0.027    0.003    9.724    0.000
##  .trststate ~~                                        
##    .trstloc           0.069    0.003   19.830    0.000
##    .trstgen2          0.031    0.003   10.794    0.000
##  .trstloc ~~                                          
##    .trstgen           0.037    0.004    8.346    0.000
##    .trstgen2          0.037    0.003   12.723    0.000
##  .trstgen ~~                                          
##    .trstgen2          0.077    0.005   16.991    0.000
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.559    0.009   64.308    0.000
##    .ptgi_2            0.454    0.009   50.813    0.000
##    .ptgi_3            0.446    0.010   45.193    0.000
##    .ptgi_4            0.509    0.009   55.214    0.000
##    .ptgi_5            0.371    0.009   39.808    0.000
##    .ptsi_1            0.241    0.008   29.753    0.000
##    .ptsi_2            0.221    0.008   26.925    0.000
##    .ptsi_3            0.280    0.009   31.927    0.000
##    .ptsi_4            0.243    0.009   27.904    0.000
##    .ptsi_5            0.307    0.009   34.859    0.000
##    .trstgov           0.343    0.025   13.903    0.000
##    .trststate         0.374    0.026   14.614    0.000
##    .trstloc           0.425    0.026   16.633    0.000
##    .trstgen           0.348    0.038    9.220    0.000
##    .trstgen2          0.537    0.024   22.531    0.000
##     gop               0.353    0.013   27.187    0.000
##     black             0.117    0.009   13.375    0.000
##     income            0.480    0.008   57.292    0.000
##     ptgi1             0.000                           
##     ptsi1             0.000                           
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .ptgi_1            0.049    0.002   22.411    0.000
##    .ptgi_2            0.034    0.002   18.812    0.000
##    .ptgi_3            0.058    0.003   21.906    0.000
##    .ptgi_4            0.039    0.002   19.753    0.000
##    .ptgi_5            0.042    0.002   19.988    0.000
##    .ptsi_1            0.042    0.002   22.716    0.000
##    .ptsi_2            0.027    0.001   18.824    0.000
##    .ptsi_3            0.034    0.002   19.714    0.000
##    .ptsi_4            0.030    0.002   18.403    0.000
##    .ptsi_5            0.055    0.002   23.342    0.000
##    .trstgov           0.098    0.004   25.435    0.000
##    .trststate         0.105    0.004   25.590    0.000
##    .trstloc           0.104    0.004   25.414    0.000
##    .trstgen           0.233    0.009   25.929    0.000
##    .trstgen2          0.091    0.004   25.681    0.000
##     gop               0.228    0.009   26.010    0.000
##     black             0.103    0.004   26.010    0.000
##     income            0.087    0.004   24.811    0.000
##     ptgi1             0.053    0.004   14.643    0.000
##     ptsi1             0.046    0.003   14.769    0.000</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;Pooled Studies II-V&#39; = fit_r1),
             output = &#39;latex&#39;,
             coef_omit = c(1:10, 18:20, 23:25, 28:30, 33:37, 39:88),
             # coef_rename = c(&#39;Non-Black&#39; = &#39;Group1&#39;,
             #                  &#39;Black&#39; = &#39;Group 2&#39;),
             stars = c(&#39;*&#39; = 0.05),
             gof_omit = &#39;AIC|BIC|F|Log.Lik.&#39;,
             escape = F)</code></pre>
<pre><code>## Warning: There are duplicate term names in the table.
##   The `shape` argument of the `modelsummary` function can be used to print
##   related terms together. The `group_map` argument can be used to reorder,
##   subset, and rename group identifiers. See `?modelsummary` for details.
##   You can find the group identifier to use in the `shape` argument by
##   calling `get_estimates()` on one of your models. Candidates include:
##   component, group, s.value</code></pre>
</div>
<div id="modeling-voting" class="section level2">
<h2>Modeling Voting</h2>
<div id="path-model-approach" class="section level3">
<h3>Path Model Approach</h3>
<p>Stress consistently associated with increased trust in government and
decreased likelihood of voting in 2016 across samples 2-5. Significant
PTSI–2016 voting in samples 2, 3, 5 and PTSI–2020 voting in samples 2,
3, 4, 5. PTSI–trust in federal government is significant in 3, 4, 5.
PTGI is not significantly associated with trust in the federal
government or with voting in 2016 or 2020 in any samples.</p>
<p>Multiple group SEM to identify if effect is divergent for Black and
White respondents. Due to representativeness of sample, number of Black
respondents is low. I pool samples 2 and 3 as well as 4 and 5 given they
were fielded at the same time and to increase sample size of Black
respondents. No significant differences between Black and White
respondents in terms of these associations.</p>
<p>I then check for female versus male respondents and income
groups.</p>
<p>For income levels, I bisect the samples comparing those who make less
than $50,000 per year in family income to those who make $50,000 or more
per year in family income. In terms of significant associations:
PTSI–2016 voting</p>
<pre class="r"><code>## PTSI

med_1s &lt;- &#39;# direct effect
        presvote16 ~ c*ptsi
        # mediator
        trstgov ~ a*ptsi
        presvote16 ~ b*trstgov
        # indirect effect
        ab := a*b
        # total effect
        total := c + (a*b)
        &#39;
med_2s &lt;- &#39;# direct effect
        presvote20 ~ c*ptsi
        # mediator
        trstgov ~ a*ptsi
        presvote20 ~ b*trstgov
        # indirect effect
        ab := a*b
        # total effect
        total := c + (a*b)
        &#39;

med2_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat2)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 235 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med2_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat2)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 235 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med3_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat3)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 269 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med3_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat3)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 269 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med4_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat4)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 248 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med4_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat4)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 248 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med5_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat5)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 227 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med5_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat5)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 227 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>summary(med2_1s); summary(med2_2s); summary(med3_1s); summary(med3_2s); summary(med4_1s); summary(med4_2s); summary(med5_1s); summary(med5_2s)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           765        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptsi       (c)   -0.208    0.075   -2.753    0.006
##   trstgov ~                                           
##     ptsi       (a)    0.073    0.047    1.542    0.123
##   presvote16 ~                                        
##     trstgov    (b)    0.007    0.068    0.097    0.923
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.706    0.038   18.364    0.000
##    .trstgov           0.321    0.019   17.274    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.227    0.006   35.973    0.000
##    .trstgov           0.093    0.005   19.104    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab                0.000    0.005    0.097    0.923
##     total            -0.207    0.075   -2.750    0.006</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           765        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptsi       (c)   -0.176    0.068   -2.601    0.009
##   trstgov ~                                           
##     ptsi       (a)    0.073    0.047    1.539    0.124
##   presvote20 ~                                        
##     trstgov    (b)   -0.016    0.059   -0.268    0.788
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.834    0.034   24.321    0.000
##    .trstgov           0.321    0.019   17.266    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.173    0.010   16.636    0.000
##    .trstgov           0.093    0.005   19.104    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.001    0.004   -0.268    0.788
##     total            -0.177    0.068   -2.606    0.009</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           731        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptsi       (c)   -0.175    0.090   -1.949    0.051
##   trstgov ~                                           
##     ptsi       (a)    0.179    0.059    3.010    0.003
##   presvote16 ~                                        
##     trstgov    (b)   -0.124    0.078   -1.597    0.110
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.700    0.045   15.451    0.000
##    .trstgov           0.318    0.024   13.266    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.236    0.006   40.721    0.000
##    .trstgov           0.105    0.006   16.749    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.022    0.016   -1.428    0.153
##     total            -0.197    0.089   -2.213    0.027</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           731        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptsi       (c)   -0.180    0.076   -2.371    0.018
##   trstgov ~                                           
##     ptsi       (a)    0.178    0.060    2.993    0.003
##   presvote20 ~                                        
##     trstgov    (b)   -0.121    0.063   -1.941    0.052
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.885    0.031   28.517    0.000
##    .trstgov           0.319    0.024   13.272    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.166    0.012   13.750    0.000
##    .trstgov           0.105    0.006   16.748    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.022    0.012   -1.738    0.082
##     total            -0.202    0.075   -2.703    0.007</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           752        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptsi       (c)   -0.030    0.085   -0.352    0.725
##   trstgov ~                                           
##     ptsi       (a)    0.216    0.060    3.606    0.000
##   presvote16 ~                                        
##     trstgov    (b)   -0.125    0.068   -1.830    0.067
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.793    0.042   18.974    0.000
##    .trstgov           0.316    0.023   13.823    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.191    0.011   17.139    0.000
##    .trstgov           0.109    0.005   20.074    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.027    0.016   -1.705    0.088
##     total            -0.057    0.083   -0.685    0.493</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           752        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptsi       (c)   -0.147    0.072   -2.025    0.043
##   trstgov ~                                           
##     ptsi       (a)    0.217    0.060    3.623    0.000
##   presvote20 ~                                        
##     trstgov    (b)    0.013    0.057    0.231    0.817
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.878    0.041   21.595    0.000
##    .trstgov           0.316    0.023   13.826    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.132    0.014    9.527    0.000
##    .trstgov           0.109    0.005   20.075    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab                0.003    0.012    0.230    0.818
##     total            -0.144    0.074   -1.935    0.053</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           773        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptsi       (c)   -0.206    0.083   -2.499    0.012
##   trstgov ~                                           
##     ptsi       (a)    0.154    0.059    2.608    0.009
##   presvote16 ~                                        
##     trstgov    (b)    0.050    0.062    0.808    0.419
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.791    0.039   20.228    0.000
##    .trstgov           0.328    0.023   14.324    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.186    0.010   17.929    0.000
##    .trstgov           0.107    0.006   18.431    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab                0.008    0.010    0.777    0.437
##     total            -0.199    0.083   -2.382    0.017</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           773        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptsi       (c)   -0.315    0.074   -4.231    0.000
##   trstgov ~                                           
##     ptsi       (a)    0.154    0.059    2.605    0.009
##   presvote20 ~                                        
##     trstgov    (b)   -0.009    0.050   -0.182    0.855
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.947    0.025   37.855    0.000
##    .trstgov           0.328    0.023   14.325    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.120    0.011   10.618    0.000
##    .trstgov           0.107    0.006   18.432    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.001    0.008   -0.180    0.857
##     total            -0.316    0.073   -4.328    0.000</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;2016&#39;  = med2_1s,
                           &#39;2020&#39;  = med2_2s,
                           &#39;2016&#39;  = med3_1s,
                           &#39;2020&#39;  = med3_2s,
                           &#39;2016&#39;  = med4_1s,
                           &#39;2020&#39;  = med4_2s,
                           &#39;2016&#39;  = med5_1s,
                           &#39;2020&#39;  = med5_2s),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code>## PTGI

med_1g &lt;- &#39;# direct effect
        presvote16 ~ c*ptgi
        # mediator
        trstgov ~ a*ptgi
        presvote16 ~ b*trstgov
        # indirect effect
        ab := a*b
        # total effect
        total := c + (a*b)
        &#39;
med_2g &lt;- &#39;# direct effect
        presvote20 ~ c*ptgi
        # mediator
        trstgov ~ a*ptgi
        presvote20 ~ b*trstgov
        # indirect effect
        ab := a*b
        # total effect
        total := c + (a*b)
        &#39;

med2_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat2)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 235 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med2_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat2)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 235 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med3_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat3)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 268 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med3_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat3)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 268 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med4_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat4)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 248 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med4_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat4)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 248 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med5_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat5)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 227 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>med5_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, data = dat5)</code></pre>
<pre><code>## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: 227 cases were deleted due to missing values in 
##        exogenous variable(s), while fixed.x = TRUE.</code></pre>
<pre class="r"><code>summary(med2_1g); summary(med2_2g); summary(med3_1g); summary(med3_2g); summary(med4_1g); summary(med4_2g); summary(med5_1g); summary(med5_2g)</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           765        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptgi       (c)   -0.115    0.075   -1.523    0.128
##   trstgov ~                                           
##     ptgi       (a)    0.010    0.042    0.241    0.809
##   presvote16 ~                                        
##     trstgov    (b)   -0.004    0.069   -0.054    0.957
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.704    0.048   14.629    0.000
##    .trstgov           0.339    0.023   14.500    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.229    0.006   37.763    0.000
##    .trstgov           0.093    0.005   19.330    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.000    0.001   -0.053    0.958
##     total            -0.115    0.075   -1.525    0.127</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           765        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptgi       (c)   -0.071    0.069   -1.026    0.305
##   trstgov ~                                           
##     ptgi       (a)    0.010    0.043    0.235    0.814
##   presvote20 ~                                        
##     trstgov    (b)   -0.027    0.060   -0.445    0.656
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.819    0.045   18.117    0.000
##    .trstgov           0.339    0.023   14.502    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.175    0.011   16.612    0.000
##    .trstgov           0.093    0.005   19.329    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.000    0.001   -0.216    0.829
##     total            -0.072    0.069   -1.031    0.303</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           732        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptgi       (c)   -0.011    0.090   -0.124    0.901
##   trstgov ~                                           
##     ptgi       (a)    0.042    0.055    0.775    0.438
##   presvote16 ~                                        
##     trstgov    (b)   -0.144    0.077   -1.869    0.062
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.656    0.061   10.734    0.000
##    .trstgov           0.354    0.029   12.089    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.238    0.005   44.297    0.000
##    .trstgov           0.108    0.006   17.730    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.006    0.008   -0.758    0.448
##     total            -0.017    0.090   -0.192    0.848</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           732        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptgi       (c)   -0.066    0.072   -0.908    0.364
##   trstgov ~                                           
##     ptgi       (a)    0.041    0.055    0.751    0.453
##   presvote20 ~                                        
##     trstgov    (b)   -0.142    0.061   -2.323    0.020
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.870    0.045   19.436    0.000
##    .trstgov           0.354    0.029   12.112    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.168    0.012   13.692    0.000
##    .trstgov           0.108    0.006   17.730    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.006    0.008   -0.752    0.452
##     total            -0.072    0.073   -0.976    0.329</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           752        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptgi       (c)    0.058    0.074    0.779    0.436
##   trstgov ~                                           
##     ptgi       (a)    0.078    0.060    1.300    0.194
##   presvote16 ~                                        
##     trstgov    (b)   -0.133    0.068   -1.953    0.051
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.759    0.046   16.317    0.000
##    .trstgov           0.336    0.033   10.128    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.191    0.011   17.136    0.000
##    .trstgov           0.111    0.006   20.048    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.010    0.010   -1.049    0.294
##     total             0.047    0.075    0.629    0.529</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           752        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptgi       (c)    0.055    0.070    0.781    0.435
##   trstgov ~                                           
##     ptgi       (a)    0.078    0.060    1.304    0.192
##   presvote20 ~                                        
##     trstgov    (b)   -0.009    0.058   -0.151    0.880
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.817    0.051   16.025    0.000
##    .trstgov           0.335    0.033   10.113    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.133    0.014    9.821    0.000
##    .trstgov           0.111    0.006   20.049    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.001    0.005   -0.150    0.880
##     total             0.054    0.070    0.772    0.440</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           773        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote16 ~                                        
##     ptgi       (c)   -0.100    0.075   -1.334    0.182
##   trstgov ~                                           
##     ptgi       (a)    0.061    0.051    1.181    0.238
##   presvote16 ~                                        
##     trstgov    (b)    0.035    0.062    0.557    0.577
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.786    0.047   16.841    0.000
##    .trstgov           0.342    0.030   11.500    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote16        0.188    0.010   18.294    0.000
##    .trstgov           0.108    0.006   18.855    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab                0.002    0.004    0.504    0.614
##     total            -0.098    0.075   -1.305    0.192</code></pre>
<pre><code>## lavaan 0.6.16 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           773        1000
##   Number of missing patterns                         2            
##   Sampling weights variable                     weight            
## 
## Model Test User Model:
##                                               Standard      Scaled
##   Test Statistic                                 0.000       0.000
##   Degrees of freedom                                 0           0
## 
## Parameter Estimates:
## 
##   Standard errors                             Sandwich
##   Information bread                           Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##   presvote20 ~                                        
##     ptgi       (c)   -0.157    0.067   -2.340    0.019
##   trstgov ~                                           
##     ptgi       (a)    0.062    0.051    1.201    0.230
##   presvote20 ~                                        
##     trstgov    (b)   -0.034    0.051   -0.667    0.505
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.944    0.037   25.262    0.000
##    .trstgov           0.342    0.030   11.496    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##    .presvote20        0.125    0.012   10.445    0.000
##    .trstgov           0.108    0.006   18.857    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(&gt;|z|)
##     ab               -0.002    0.004   -0.590    0.555
##     total            -0.159    0.067   -2.381    0.017</code></pre>
<pre class="r"><code>modelsummary(models = list(&#39;2016&#39;  = med2_1g,
                           &#39;2020&#39;  = med2_2g,
                           &#39;2016&#39;  = med3_1g,
                           &#39;2020&#39;  = med3_2g,
                           &#39;2016&#39;  = med4_1g,
                           &#39;2020&#39;  = med4_2g,
                           &#39;2016&#39;  = med5_1g,
                           &#39;2020&#39;  = med5_2g),
             output = &#39;latex&#39;,
             stars = c(&#39;*&#39; = .05),
             gof_omit = &#39;AIC|BIC|F|RMSE|Log.Lik.&#39;,
             escape = FALSE)</code></pre>
<pre class="r"><code># ### Multiple Group SEM
# 
# #### Race
# 
# dat23 &lt;- rbind(dat2, dat3)
# dat45 &lt;- rbind(dat4, dat5)
# 
# grp2_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp2_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp4_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# grp4_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# summary(grp2_1s); summary(grp2_2s); summary(grp4_1s); summary(grp4_2s)
# 
# grp2_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp2_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp4_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# grp4_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# summary(grp2_1g); summary(grp2_2g); summary(grp4_1g); summary(grp4_2g)

# #### Woman
# 
# dat23 &lt;- rbind(dat2, dat3)
# dat45 &lt;- rbind(dat4, dat5)
# 
# grp2_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp2_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp4_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# grp4_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# summary(grp2_1s); summary(grp2_2s); summary(grp4_1s); summary(grp4_2s)
# 
# grp2_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp2_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp4_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# grp4_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# summary(grp2_1g); summary(grp2_2g); summary(grp4_1g); summary(grp4_2g)
# 
# #### Income
# 
# dat2$class &lt;- ifelse(dat2$income &lt; 0.3, 0, 1)
# dat3$class &lt;- ifelse(dat3$income &lt; 0.3, 0, 1)
# dat4$class &lt;- ifelse(dat4$income &lt; 0.3, 0, 1)
# dat5$class &lt;- ifelse(dat5$income &lt; 0.3, 0, 1)
# 
# grp2_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat2)
# 
# grp2_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat2)
# 
# grp3_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat3)
# 
# grp3_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat3)
# 
# grp4_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat4)
# 
# grp4_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat4)
# 
# grp5_1s &lt;- sem(med_1s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat5)
# 
# grp5_2s &lt;- sem(med_2s, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;class&#39;, data = dat5)
# 
# summary(grp2_1s); summary(grp2_2s); summary(grp3_1s); summary(grp3_2s); summary(grp4_1s); summary(grp4_2s); summary(grp5_1s); summary(grp5_2s)
# 
# grp2_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp2_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat23)
# 
# grp4_1g &lt;- sem(med_1g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# grp4_2g &lt;- sem(med_2g, missing = &#39;fiml&#39;, sampling.weights = &#39;weight&#39;, group = &#39;black&#39;, data = dat45)
# 
# summary(grp2_1g); summary(grp2_2g); summary(grp4_1g); summary(grp4_2g)</code></pre>
</div>
</div>
<div id="temporal-proximity" class="section level2">
<h2>Temporal Proximity</h2>
<pre class="r"><code># Trust

#self exposure time
t2_t &lt;- glm(trstgov ~ tr_exp_t + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_t &lt;- glm(trstgov ~ tr_exp_t + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_t &lt;- glm(trstgov ~ tr_exp_t + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_t &lt;- glm(trstgov ~ tr_exp_t + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_t); summary(t3_t); summary(t4_t); summary(t5_t)</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat2, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept)  0.01356    0.31039   0.044   0.9651  
## tr_exp_t    -0.57277    0.26780  -2.139   0.0325 *
## age         -0.16242    0.37683  -0.431   0.6665  
## edu         -0.23034    0.31199  -0.738   0.4603  
## female      -0.06730    0.18084  -0.372   0.7098  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 301.32  on 583  degrees of freedom
## Residual deviance: 294.95  on 579  degrees of freedom
##   (416 observations deleted due to missingness)
## AIC: 616.83
## 
## Number of Fisher Scoring iterations: 3</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat3, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept)  0.03391    0.30970   0.109   0.9128  
## tr_exp_t    -0.19130    0.27756  -0.689   0.4907  
## age         -0.87593    0.40544  -2.160   0.0307 *
## edu         -0.01828    0.29027  -0.063   0.9498  
## female      -0.07060    0.18154  -0.389   0.6973  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 305.86  on 552  degrees of freedom
## Residual deviance: 299.17  on 548  degrees of freedom
##   (447 observations deleted due to missingness)
## AIC: 598.29
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept) -0.07398    0.33096  -0.224   0.8231  
## tr_exp_t    -0.55843    0.26111  -2.139   0.0325 *
## age         -1.00775    0.41542  -2.426   0.0153 *
## edu          0.59071    0.34431   1.716   0.0862 .
## female      -0.05973    0.18425  -0.324   0.7458  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 345.43  on 568  degrees of freedom
## Residual deviance: 328.37  on 564  degrees of freedom
##   (431 observations deleted due to missingness)
## AIC: 607.39
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat5, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -0.7066     0.3136  -2.253 0.024256 *  
## tr_exp_t     -0.5495     0.2804  -1.960 0.050004 .  
## age          -0.5483     0.4009  -1.368 0.171435    
## edu           1.1854     0.3407   3.480 0.000502 ***
## female        0.1426     0.1790   0.797 0.425688    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 356.00  on 593  degrees of freedom
## Residual deviance: 336.32  on 589  degrees of freedom
##   (406 observations deleted due to missingness)
## AIC: 606.36
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code># close friend/family member exposure time
t2_rt &lt;- glm(trstgov ~ rtr_exp_t + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_rt &lt;- glm(trstgov ~ rtr_exp_t + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_rt &lt;- glm(trstgov ~ rtr_exp_t + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_rt &lt;- glm(trstgov ~ rtr_exp_t + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_rt); summary(t3_rt); summary(t4_rt); summary(t5_rt)</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat2, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)
## (Intercept) -0.23511    0.28981  -0.811    0.417
## rtr_exp_t   -0.35186    0.25221  -1.395    0.163
## age         -0.11688    0.33238  -0.352    0.725
## edu         -0.16071    0.29399  -0.547    0.585
## female       0.03658    0.16872   0.217    0.828
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 327.24  on 638  degrees of freedom
## Residual deviance: 324.59  on 634  degrees of freedom
##   (361 observations deleted due to missingness)
## AIC: 695.21
## 
## Number of Fisher Scoring iterations: 3</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat3, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept)  -0.1723     0.2882  -0.598   0.5499  
## rtr_exp_t    -0.1733     0.2523  -0.687   0.4921  
## age          -0.6868     0.3761  -1.826   0.0679 .
## edu           0.2546     0.2851   0.893   0.3719  
## female       -0.1705     0.1736  -0.982   0.3261  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 345.56  on 601  degrees of freedom
## Residual deviance: 339.46  on 597  degrees of freedom
##   (398 observations deleted due to missingness)
## AIC: 675.61
## 
## Number of Fisher Scoring iterations: 3</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept) -0.09685    0.30153  -0.321   0.7481  
## rtr_exp_t   -0.08404    0.25342  -0.332   0.7402  
## age         -0.77000    0.36878  -2.088   0.0368 *
## edu          0.08373    0.30130   0.278   0.7811  
## female      -0.09647    0.17135  -0.563   0.5735  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 346.64  on 599  degrees of freedom
## Residual deviance: 340.94  on 595  degrees of freedom
##   (400 observations deleted due to missingness)
## AIC: 672.24
## 
## Number of Fisher Scoring iterations: 3</code></pre>
<pre><code>## 
## Call:
## glm(formula = trstgov ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat5, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)   
## (Intercept) -0.52743    0.31560  -1.671  0.09469 . 
## rtr_exp_t   -0.56160    0.25966  -2.163  0.03055 * 
## age         -0.30927    0.36065  -0.858  0.39114   
## edu          0.89254    0.32913   2.712  0.00669 **
## female       0.01628    0.17068   0.095  0.92402   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 368.64  on 632  degrees of freedom
## Residual deviance: 355.26  on 628  degrees of freedom
##   (367 observations deleted due to missingness)
## AIC: 654.61
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code># turnout
# trauma exposure time
t2_tr &lt;- glm(presvote20 ~ tr_exp_t + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_tr &lt;- glm(presvote20 ~ tr_exp_t + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_tr &lt;- glm(presvote20 ~ tr_exp_t + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_tr &lt;- glm(presvote20 ~ tr_exp_t + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_tr); summary(t3_tr); summary(t4_tr); summary(t5_tr)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat2, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -1.0446     0.3623  -2.883  0.00394 ** 
## tr_exp_t     -0.4147     0.3383  -1.226  0.22021    
## age           4.1755     0.5544   7.531 5.03e-14 ***
## edu           2.3068     0.4014   5.746 9.12e-09 ***
## female       -0.1895     0.2313  -0.819  0.41253    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 595.50  on 592  degrees of freedom
## Residual deviance: 481.61  on 588  degrees of freedom
##   (407 observations deleted due to missingness)
## AIC: 531
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat3, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -1.9211     0.4216  -4.556 5.21e-06 ***
## tr_exp_t      0.1979     0.3921   0.505  0.61383    
## age           5.7473     0.7169   8.017 1.08e-15 ***
## edu           3.1510     0.4369   7.212 5.51e-13 ***
## female       -0.6983     0.2671  -2.615  0.00893 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 566.18  on 563  degrees of freedom
## Residual deviance: 392.57  on 559  degrees of freedom
##   (436 observations deleted due to missingness)
## AIC: 439.46
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -0.6738     0.3914  -1.721   0.0852 .  
## tr_exp_t     -0.1258     0.3373  -0.373   0.7093    
## age           5.4030     0.6737   8.020 1.06e-15 ***
## edu           1.1204     0.4569   2.452   0.0142 *  
## female       -0.3324     0.2526  -1.316   0.1882    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 516.93  on 595  degrees of freedom
## Residual deviance: 419.97  on 591  degrees of freedom
##   (404 observations deleted due to missingness)
## AIC: 440.59
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat5, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -0.5803     0.3949  -1.469 0.141736    
## tr_exp_t      1.1514     0.3506   3.284 0.001024 ** 
## age           3.7356     0.6307   5.923 3.16e-09 ***
## edu           1.5129     0.4498   3.364 0.000769 ***
## female       -1.2161     0.2759  -4.408 1.04e-05 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 525.10  on 604  degrees of freedom
## Residual deviance: 407.43  on 600  degrees of freedom
##   (395 observations deleted due to missingness)
## AIC: 418.16
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code># trauma exposure time interacted with PTSI
t2_tr2 &lt;- glm(presvote20 ~ tr_exp_t*ptsi + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_tr2 &lt;- glm(presvote20 ~ tr_exp_t*ptsi + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_tr2 &lt;- glm(presvote20 ~ tr_exp_t*ptsi + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_tr2 &lt;- glm(presvote20 ~ tr_exp_t*ptsi + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_tr2); summary(t3_tr2); summary(t4_tr2); summary(t5_tr2)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptsi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat2, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)   -1.13564    0.64837  -1.752   0.0799 .  
## tr_exp_t      -0.08915    0.71445  -0.125   0.9007    
## ptsi           0.18758    1.05961   0.177   0.8595    
## age            4.08074    0.55939   7.295 2.99e-13 ***
## edu            2.29321    0.40297   5.691 1.26e-08 ***
## female        -0.13551    0.23718  -0.571   0.5678    
## tr_exp_t:ptsi -0.80215    1.26233  -0.635   0.5251    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 595.50  on 592  degrees of freedom
## Residual deviance: 480.26  on 586  degrees of freedom
##   (407 observations deleted due to missingness)
## AIC: 533.37
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptsi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat3, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    -1.3787     0.7824  -1.762  0.07805 .  
## tr_exp_t       -0.4839     0.8097  -0.598  0.55012    
## ptsi           -1.1250     1.3121  -0.857  0.39122    
## age             5.7649     0.7247   7.955 1.79e-15 ***
## edu             3.1424     0.4383   7.169 7.53e-13 ***
## female         -0.7053     0.2699  -2.613  0.00898 ** 
## tr_exp_t:ptsi   1.5051     1.5099   0.997  0.31886    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 566.07  on 562  degrees of freedom
## Residual deviance: 391.21  on 556  degrees of freedom
##   (437 observations deleted due to missingness)
## AIC: 442.64
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptsi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    -0.2959     0.6220  -0.476    0.634    
## tr_exp_t       -0.5587     0.6327  -0.883    0.377    
## ptsi           -0.7654     0.9573  -0.800    0.424    
## age             5.3977     0.6817   7.918 2.41e-15 ***
## edu             1.1135     0.4577   2.433    0.015 *  
## female         -0.3461     0.2576  -1.343    0.179    
## tr_exp_t:ptsi   0.9553     1.1927   0.801    0.423    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 516.93  on 595  degrees of freedom
## Residual deviance: 419.28  on 589  degrees of freedom
##   (404 observations deleted due to missingness)
## AIC: 444.56
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptsi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat5, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    0.09543    0.58078   0.164  0.86948    
## tr_exp_t       0.75314    0.65274   1.154  0.24858    
## ptsi          -1.38037    0.87344  -1.580  0.11402    
## age            3.47717    0.65787   5.286 1.25e-07 ***
## edu            1.48338    0.45623   3.251  0.00115 ** 
## female        -1.13862    0.27840  -4.090 4.32e-05 ***
## tr_exp_t:ptsi  0.75713    1.17823   0.643  0.52048    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 525.10  on 604  degrees of freedom
## Residual deviance: 403.21  on 598  degrees of freedom
##   (395 observations deleted due to missingness)
## AIC: 417.54
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code># trauma exposure time interacted with PTGI
t2_tr3 &lt;- glm(presvote20 ~ tr_exp_t*ptgi + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_tr3 &lt;- glm(presvote20 ~ tr_exp_t*ptgi + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_tr3 &lt;- glm(presvote20 ~ tr_exp_t*ptgi + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_tr3 &lt;- glm(presvote20 ~ tr_exp_t*ptgi + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_tr3); summary(t3_tr3); summary(t4_tr3); summary(t5_tr3)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptgi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat2, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    -1.1623     0.6612  -1.758   0.0788 .  
## tr_exp_t        0.3504     0.7561   0.463   0.6431    
## ptgi            0.2215     1.0022   0.221   0.8251    
## age             4.2620     0.5635   7.563 3.93e-14 ***
## edu             2.1854     0.4064   5.377 7.56e-08 ***
## female         -0.1024     0.2372  -0.432   0.6659    
## tr_exp_t:ptgi  -1.3813     1.1776  -1.173   0.2408    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 595.50  on 592  degrees of freedom
## Residual deviance: 475.99  on 586  degrees of freedom
##   (407 observations deleted due to missingness)
## AIC: 528.33
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptgi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat3, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    -1.6353     0.7894  -2.072   0.0383 *  
## tr_exp_t        0.1245     0.9429   0.132   0.8950    
## ptgi           -0.5565     1.2362  -0.450   0.6526    
## age             5.7933     0.7255   7.986 1.40e-15 ***
## edu             3.1578     0.4396   7.183 6.83e-13 ***
## female         -0.6728     0.2685  -2.506   0.0122 *  
## tr_exp_t:ptgi   0.1025     1.4791   0.069   0.9448    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 566.18  on 563  degrees of freedom
## Residual deviance: 391.66  on 557  degrees of freedom
##   (436 observations deleted due to missingness)
## AIC: 441.76
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptgi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)     0.1550     0.6724   0.230   0.8177    
## tr_exp_t       -1.4794     0.7672  -1.928   0.0538 .  
## ptgi           -1.5917     1.0026  -1.588   0.1124    
## age             5.4052     0.6829   7.915 2.47e-15 ***
## edu             1.2715     0.4663   2.727   0.0064 ** 
## female         -0.3508     0.2556  -1.373   0.1698    
## tr_exp_t:ptgi   2.5035     1.2148   2.061   0.0393 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 516.93  on 595  degrees of freedom
## Residual deviance: 415.37  on 589  degrees of freedom
##   (404 observations deleted due to missingness)
## AIC: 442.61
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ tr_exp_t * ptgi + age + edu + female, 
##     family = &quot;binomial&quot;, data = dat5, weights = weight)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    0.89518    0.71638   1.250  0.21145    
## tr_exp_t      -0.03928    0.86271  -0.046  0.96368    
## ptgi          -2.45196    0.95098  -2.578  0.00993 ** 
## age            3.99808    0.64592   6.190 6.03e-10 ***
## edu            1.47588    0.45663   3.232  0.00123 ** 
## female        -1.21917    0.28621  -4.260 2.05e-05 ***
## tr_exp_t:ptgi  1.78947    1.29099   1.386  0.16571    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 525.10  on 604  degrees of freedom
## Residual deviance: 397.97  on 598  degrees of freedom
##   (395 observations deleted due to missingness)
## AIC: 413.78
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code># close friend/family member trauma time
t2_rtr &lt;- glm(presvote20 ~ rtr_exp_t + age + edu + female, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t3_rtr &lt;- glm(presvote20 ~ rtr_exp_t + age + edu + female, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t4_rtr &lt;- glm(presvote20 ~ rtr_exp_t + age + edu + female, data = dat4, family = &#39;binomial&#39;, weights = as.numeric(weight))</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>t5_rtr &lt;- glm(presvote20 ~ rtr_exp_t + age + edu + female, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(t2_rtr); summary(t3_rtr); summary(t4_rtr); summary(t5_rtr)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat2, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -1.3353     0.3540  -3.772 0.000162 ***
## rtr_exp_t    -0.5797     0.3361  -1.725 0.084526 .  
## age           4.5849     0.5318   8.621  &lt; 2e-16 ***
## edu           2.7716     0.3946   7.024 2.16e-12 ***
## female       -0.0542     0.2243  -0.242 0.809026    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 681.46  on 653  degrees of freedom
## Residual deviance: 515.08  on 649  degrees of freedom
##   (346 observations deleted due to missingness)
## AIC: 554.64
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat3, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -1.4989     0.3601  -4.163 3.14e-05 ***
## rtr_exp_t     0.5020     0.3211   1.563   0.1180    
## age           4.7713     0.6113   7.805 5.93e-15 ***
## edu           2.3066     0.3820   6.037 1.57e-09 ***
## female       -0.4887     0.2364  -2.068   0.0387 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 621.48  on 611  degrees of freedom
## Residual deviance: 477.92  on 607  degrees of freedom
##   (388 observations deleted due to missingness)
## AIC: 527.21
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat4, weights = as.numeric(weight))
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept) -0.20532    0.35833  -0.573  0.56666    
## rtr_exp_t    0.05186    0.31893   0.163  0.87082    
## age          3.40902    0.55128   6.184 6.26e-10 ***
## edu          1.05110    0.40667   2.585  0.00975 ** 
## female       0.02902    0.23384   0.124  0.90124    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 531.03  on 622  degrees of freedom
## Residual deviance: 476.66  on 618  degrees of freedom
##   (377 observations deleted due to missingness)
## AIC: 500.64
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ rtr_exp_t + age + edu + female, family = &quot;binomial&quot;, 
##     data = dat5, weights = weight)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)  -0.2498     0.4326  -0.578 0.563575    
## rtr_exp_t     0.3859     0.3691   1.046 0.295732    
## age           4.9716     0.6905   7.200 6.02e-13 ***
## edu           1.3088     0.4767   2.746 0.006041 ** 
## female       -0.9261     0.2720  -3.404 0.000663 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 491.97  on 643  degrees of freedom
## Residual deviance: 384.40  on 639  degrees of freedom
##   (356 observations deleted due to missingness)
## AIC: 398.85
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<div id="regression-approach-ols-and-logit-glm" class="section level3">
<h3>Regression Approach (OLS and Logit GLM)</h3>
<pre class="r"><code>m1_2 &lt;- glm(presvote16 ~ ptgi*ptsi*trstgov, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m1_2)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote16 ~ ptgi * ptsi * trstgov, family = &quot;binomial&quot;, 
##     data = dat2, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)        1.14210    0.33083   3.452 0.000556 ***
## ptgi              -0.75282    0.58489  -1.287 0.198054    
## ptsi              -0.81835    0.87711  -0.933 0.350820    
## trstgov           -0.31283    0.82241  -0.380 0.703666    
## ptgi:ptsi          0.63382    1.38745   0.457 0.647797    
## ptgi:trstgov       1.18422    1.41095   0.839 0.401296    
## ptsi:trstgov      -0.04602    2.11673  -0.022 0.982653    
## ptgi:ptsi:trstgov -1.45676    3.41731  -0.426 0.669897    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 962.35  on 747  degrees of freedom
## Residual deviance: 950.58  on 740  degrees of freedom
##   (252 observations deleted due to missingness)
## AIC: 1012.7
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code>m2_2 &lt;- glm(presvote20 ~ ptgi*ptsi*trstgov + presvote16, data = dat2, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m2_2)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ ptgi * ptsi * trstgov + presvote16, 
##     family = &quot;binomial&quot;, data = dat2, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)        -0.4737     0.4675  -1.013   0.3109    
## ptgi                1.0557     0.8407   1.256   0.2092    
## ptsi               -1.0146     1.2669  -0.801   0.4233    
## trstgov             0.1819     1.1318   0.161   0.8723    
## presvote16          3.5790     0.2749  13.021   &lt;2e-16 ***
## ptgi:ptsi          -0.1536     2.0147  -0.076   0.9392    
## ptgi:trstgov       -0.8587     2.0182  -0.425   0.6705    
## ptsi:trstgov        5.4100     3.1263   1.730   0.0835 .  
## ptgi:ptsi:trstgov  -8.3857     4.9327  -1.700   0.0891 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 798.90  on 747  degrees of freedom
## Residual deviance: 501.23  on 739  degrees of freedom
##   (252 observations deleted due to missingness)
## AIC: 507.18
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code>m1_3 &lt;- glm(presvote16 ~ ptgi*ptsi*trstgov, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m1_3)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote16 ~ ptgi * ptsi * trstgov, family = &quot;binomial&quot;, 
##     data = dat3, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)
## (Intercept)         0.5465     0.3522   1.552    0.121
## ptgi                0.6759     0.6348   1.065    0.287
## ptsi                0.1651     0.9942   0.166    0.868
## trstgov             0.1431     0.8082   0.177    0.859
## ptgi:ptsi          -1.8424     1.6069  -1.147    0.252
## ptgi:trstgov       -1.4701     1.3985  -1.051    0.293
## ptsi:trstgov       -2.3183     2.1804  -1.063    0.288
## ptgi:ptsi:trstgov   4.4734     3.1868   1.404    0.160
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 944.06  on 716  degrees of freedom
## Residual deviance: 928.71  on 709  degrees of freedom
##   (283 observations deleted due to missingness)
## AIC: 992.38
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code>m2_3 &lt;- glm(presvote20 ~ ptgi*ptsi*trstgov + presvote16, data = dat3, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m2_3)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ ptgi * ptsi * trstgov + presvote16, 
##     family = &quot;binomial&quot;, data = dat3, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)         1.5615     0.6243   2.501  0.01238 *  
## ptgi               -1.5657     1.0951  -1.430  0.15281    
## ptsi               -4.3354     1.4866  -2.916  0.00354 ** 
## trstgov            -0.4353     1.3747  -0.317  0.75153    
## presvote16          3.5576     0.3233  11.003  &lt; 2e-16 ***
## ptgi:ptsi           5.1646     2.4326   2.123  0.03375 *  
## ptgi:trstgov       -1.2148     2.2853  -0.532  0.59501    
## ptsi:trstgov        1.5000     3.2004   0.469  0.63929    
## ptgi:ptsi:trstgov  -0.2048     4.5571  -0.045  0.96416    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 732.02  on 716  degrees of freedom
## Residual deviance: 475.73  on 708  degrees of freedom
##   (283 observations deleted due to missingness)
## AIC: 527.18
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code>m1_4 &lt;- glm(presvote16 ~ ptgi*ptsi*trstgov, data = dat4, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m1_4)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote16 ~ ptgi * ptsi * trstgov, family = &quot;binomial&quot;, 
##     data = dat4, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)         1.2289     0.3710   3.312 0.000925 ***
## ptgi                0.6633     0.7027   0.944 0.345214    
## ptsi                1.1515     1.1680   0.986 0.324176    
## trstgov            -1.2545     0.7502  -1.672 0.094485 .  
## ptgi:ptsi          -3.7342     1.9009  -1.964 0.049478 *  
## ptgi:trstgov        0.3297     1.4079   0.234 0.814837    
## ptsi:trstgov       -1.5519     2.2802  -0.681 0.496122    
## ptgi:ptsi:trstgov   5.1617     3.6741   1.405 0.160054    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 808.63  on 720  degrees of freedom
## Residual deviance: 790.00  on 713  degrees of freedom
##   (279 observations deleted due to missingness)
## AIC: 847.02
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code>m2_4 &lt;- glm(presvote20 ~ ptgi*ptsi*trstgov + presvote16, data = dat4, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m2_4)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ ptgi * ptsi * trstgov + presvote16, 
##     family = &quot;binomial&quot;, data = dat4, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)       -0.55234    0.53416  -1.034    0.301    
## ptgi               1.34467    0.99447   1.352    0.176    
## ptsi               0.99206    1.57764   0.629    0.529    
## trstgov            1.44399    1.19808   1.205    0.228    
## presvote16         2.71775    0.25692  10.578   &lt;2e-16 ***
## ptgi:ptsi         -2.74161    2.64786  -1.035    0.300    
## ptgi:trstgov       0.02475    2.24344   0.011    0.991    
## ptsi:trstgov      -3.65118    3.24690  -1.125    0.261    
## ptgi:ptsi:trstgov  2.11887    5.21881   0.406    0.685    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 608.03  on 720  degrees of freedom
## Residual deviance: 457.02  on 712  degrees of freedom
##   (279 observations deleted due to missingness)
## AIC: 497.85
## 
## Number of Fisher Scoring iterations: 5</code></pre>
<pre class="r"><code>m1_5 &lt;- glm(presvote16 ~ ptgi*ptsi*trstgov, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m1_5)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote16 ~ ptgi * ptsi * trstgov, family = &quot;binomial&quot;, 
##     data = dat5, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)        1.33448    0.37022   3.605 0.000313 ***
## ptgi               0.22153    0.71106   0.312 0.755378    
## ptsi               0.06489    1.01772   0.064 0.949158    
## trstgov            1.17752    0.89076   1.322 0.186191    
## ptgi:ptsi         -2.72647    1.83756  -1.484 0.137877    
## ptgi:trstgov      -2.55774    1.68886  -1.514 0.129906    
## ptsi:trstgov      -4.40717    2.42519  -1.817 0.069179 .  
## ptgi:ptsi:trstgov  9.75059    4.07338   2.394 0.016678 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 813.79  on 758  degrees of freedom
## Residual deviance: 793.87  on 751  degrees of freedom
##   (241 observations deleted due to missingness)
## AIC: 811.37
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code>m2_5 &lt;- glm(presvote20 ~ ptgi*ptsi*trstgov + presvote16, data = dat5, family = &#39;binomial&#39;, weights = weight)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code>summary(m2_5)</code></pre>
<pre><code>## 
## Call:
## glm(formula = presvote20 ~ ptgi * ptsi * trstgov + presvote16, 
##     family = &quot;binomial&quot;, data = dat5, weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)        1.16540    0.67971   1.715   0.0864 .  
## ptgi               0.23340    1.20137   0.194   0.8460    
## ptsi              -2.54178    1.44828  -1.755   0.0793 .  
## trstgov            2.37131    1.76249   1.345   0.1785    
## presvote16         3.02618    0.28721  10.537   &lt;2e-16 ***
## ptgi:ptsi          0.05141    2.56561   0.020   0.9840    
## ptgi:trstgov      -5.99522    2.98350  -2.009   0.0445 *  
## ptsi:trstgov      -4.19586    3.71520  -1.129   0.2587    
## ptgi:ptsi:trstgov  9.56974    6.06103   1.579   0.1144    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 580.82  on 758  degrees of freedom
## Residual deviance: 385.05  on 750  degrees of freedom
##   (241 observations deleted due to missingness)
## AIC: 399.44
## 
## Number of Fisher Scoring iterations: 6</code></pre>
<pre class="r"><code># Trust in Federal Government, by party

m1_1 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income + gop,
           data = dat1)

m1r_1 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat1, gop == 1))
m1d_1 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat1, gop == 0))
m1r_2 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat2, gop == 1))
m1d_2 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat2, gop == 0))
m1r_3 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat3, gop == 1))
m1d_3 &lt;- lm(trstgov ~ ptsi + ptgi + female + black + income,
           data = subset(dat3, gop == 0))

m2_1 &lt;- lm(trststate ~ ptsi + ptgi + female + black + income + gop,
           data = dat1)

m3_1 &lt;- glm(trstgen ~ ptsi + ptgi + female + black + income + gop,
           family = &#39;binomial&#39;, data = dat1)</code></pre>
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